Image processing apparatus and method for evaluating objects in an image

ABSTRACT

The present disclosure relates to a data processing apparatus and a data processing method capable of generating evaluation index data for performing accurate and detailed evaluation of cultured cardiomyocytes. 
     A motion detecting unit divides frame image data obtained by photographing the cultured cardiomyocytes for a predetermined time into blocks and obtains motion detection data in units of blocks per each frame period. A feature amount calculating unit calculates a feature amount for each block at the same position in a frame image using the motion detection data. A classification processing unit classifies each of the blocks into any one of a plurality of classification categories using the calculated feature amount. On the basis of the classification result, evaluation index data made of individual classification result data that represent correspondences between the blocks and the classification categories is generated.

CROSS REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/669,169, filed on Aug. 4, 2017, which is incorporated herein byreference in its entirety. Application Ser. No. 15/669,169 is adivisional of U.S. patent application Ser. No. 13/637,233, filed on Dec.3, 2012, which is a national stage entry filed under 35 U.S.C. § 371 ofPCT Application No. PCT/JP2011/054557, filed Feb. 28, 2011, which claimspriority to Japanese Patent Applications JP 2010-234504, filed Oct. 19,2010, and JP 2010-074306, filed Mar. 29, 2010.

TECHNICAL FIELD

The present disclosure relates to a data processing apparatus, a dataprocessing method, an image processing apparatus and a method, and aprogram, and more particularly, to a data processing apparatus whichgenerates data that becomes an index when evaluation is performed on anobject which performs a motion having periodicity, a method thereof, animage processing apparatus and a method, and a program.

BACKGROUND ART

In the field of regenerative medicine, using cultured cells manufacturedby culturing cells, regeneration of cells, tissues, organs, and the likeof a body that are lost due to accidents, disease, and the like andrestoration of functions have been achieved. Cell tissues that may bemanufactured as such cultured cells range over many fields, andcardiomyocytes in one of the fields are used for heart treatment.Cultured cardiomyocytes themselves have motions corresponding topulsations. Here, in a manufacturing stage of cultured cardiomyocytes,for example, it is necessary to perform quality evaluation of whether ornot the motions are favorable.

When such quality evaluation of the cultured cardiomyocytes isperformed, for example, in current situations, visual observation isperformed. In addition, measuring a potential by piercing culturedcardiomyocytes with an electrode has been performed. However, visualobservation is significantly dependent on observer's opinions, and it isdifficult to obtain objective and accurate evaluation results. Inaddition, in the case of measuring a potential, the culturedcardiomyocytes come into contact with the electrode, and thus there is aproblem of not being noninvasive. In addition, information that may bequantified on the basis of the measurement of the potential is limitedonly to, for example, pulsation time.

Here, as a technique according to the related art, a configuration inwhich measurement points are set from an imaged screen obtained byphotographing a cardiomyocyte, the luminances of the measurement pointsare automatically measured, and the deformation period of thecardiomyocyte is measured from the measured values is known (forexample, refer to PTL 1).

CITATION LIST Patent Literature

PTL 1: Japanese Unexamined Patent Application Publication No. 63-233392(FIG. 1)

SUMMARY OF INVENTION Technical Problem

However, in the technique according to the related art described above,since a periodic change in luminance is an object of measurement,objects that are able to be measured are limited to the time interval ofa pulsation period. That is, though the fact that the information thatmay be quantified is limited to the period of pulsations although thetechnique is noninvasive, there are the same problems as in the case ofmeasuring the potential, and it is still difficult to obtain accurateevaluation results.

The present disclosure has been made taking the foregoing circumstancesinto consideration, and an object thereof is to perform an accurateevaluation with higher accuracy than has been carried out on the motionof an object which performs a periodic motion and is represented by acultured cardiomyocyte and the like.

Solution to Problem

The present disclosure is made to solve the problems. According to afirst aspect, a data processing apparatus includes: a motion detectingunit which divides a plurality of frame image data that form movingimage data having an image content of an object that performs a periodicmotion into blocks according to an arrangement of a predetermined pixelcount and detects time-series data of motions for each of thecorresponding blocks; a feature amount calculating unit which calculatesat least one kind of feature amount for each of the blocks on the basisof the time-series data of the motions for each of the detected blocks;and a classification processing unit which generates classification datathat represents a result of classifying each of the blocks that form anyone of the plurality of frame image data into any one of a predeterminednumber of classification categories on the basis of the calculatedfeature amount. Accordingly, there is an effect of classifying an imageof an object that performs a periodic motion according to theclassification categories set on the basis of the feature amounts.

In addition, in the first aspect, the feature amount calculating unitmay calculate a plurality of kinds of the feature amounts for each ofthe blocks, and the classification unit may generate the classificationdata on the basis of the plurality of kinds of the feature amountscalculated. Accordingly, there is an effect of classifying an image ofan object that performs a periodic motion according to theclassification categories set on the basis of a combination of theplurality of feature amounts.

In addition, in the first aspect, the feature amount calculating unitmay calculate an average motion direction which is an average value ofmotion directions per unit time within a predetermined time as one kindof the feature amounts. Accordingly, there is an effect of classifyingan image of an object that performs a periodic motion according to theclassification categories set on the basis of at least the averagemotion direction.

In addition, in the first aspect, the feature amount calculating unitmay calculate an average motion amount which is an average value ofmotion amounts per unit time within a predetermined time as one kind ofthe feature amounts. Accordingly, there is an effect of classifying animage of an object that performs a periodic motion according to theclassification categories set on the basis of at least the averagemotion amount.

In addition, in the first aspect, the feature amount calculating unitmay calculate an average amplitude which is an average value ofamplitudes with a predetermined or higher motion amount obtained withina predetermined time as one kind of the feature amounts. Accordingly,there is an effect of classifying an image of an object that performs aperiodic motion according to the classification categories set on thebasis of at least the average amplitude.

In addition, in the first aspect, the feature amount calculating unitmay calculate an average acceleration which is an average value ofaccelerations of motions per unit time within a predetermined time asone kind of the feature amounts. Accordingly, there is an effect ofclassifying an image of an object that performs a periodic motionaccording to the classification categories set on the basis of at leastthe average acceleration.

In addition, in the first aspect, the feature amount calculating unitmay calculate an average motion interval which is an average value oftime intervals at which an amplitude with a predetermined or highermotion amount is obtained within a predetermined time as one kind of thefeature amounts. Accordingly, there is an effect of classifying an imageof an object that performs a periodic motion according to theclassification categories set on the basis of at least the averagemotion interval.

In addition, in the first aspect, the feature amount calculating unitmay calculate a motion starting time which is a time from apredetermined timing to a timing at which an amplitude with apredetermined or higher motion amount is obtained as one kind of thefeature amounts. Accordingly, there is an effect of classifying an imageof an object that performs a periodic motion according to theclassification categories set on the basis of at least the motionstarting time.

In addition, in the first aspect, the classification unit may perform aprocess of calculating a distance between each of the plurality oftemplates having a combination of other feature amounts and the block incorrespondence to a plurality of the classification categories, andclassifying the block into any one of the plurality of theclassification categories on the basis of the calculated distance, oneach of the blocks. Accordingly, there is an effect of obtaining theclassification results on the basis of the distance between each of theplurality of templates and the block.

The classification unit may classify each of the blocks into any one ofa predetermined number of classification categories by performingclustering according to a k-means method on the basis of the featureamounts calculated corresponding to each of the blocks. Accordingly,there is an effect of obtaining the classification result by the k-meansmethod.

According to another aspect of the present disclosure, an imageprocessing apparatus includes: a motion detecting unit which detects amotion of an object of evaluation using an image of the object ofevaluation; an index data generating unit which, by using a motionvector that represents the motion of the object of evaluation detectedby the motion detecting unit, generates index data that represents afeature of the motion of the object of evaluation and is used as anindex for evaluating the object of evaluation; and an evaluation valuecalculating unit which evaluates the index data generated by the indexdata generating unit and calculates an evaluation value.

The index data generating unit may generate index data regarding amagnitude of an amplitude of the motion of the object of evaluation andindex data regarding a frequency per unit time of a peak of the motionof the object of evaluation, and the evaluation value calculating unitmay calculate an evaluation value that evaluates the magnitude of theamplitude of the motion of the object of evaluation using the index dataregarding the magnitude of the amplitude of the motion of the object ofevaluation generated by the index data generating unit, and may furthercalculate an evaluation value that evaluates the frequency per unit timeof the peak of the motion of the object of evaluation using the indexdata regarding the frequency per unit time of the peak of the motion ofthe object of evaluation generated by the index data generating unit.

The index data regarding the magnitude of the amplitude of the motion ofthe object of evaluation may be an average value of products of anormalized amplitude and a normalized dispersion of the amplitudes overan entire image of the object of evaluation.

The index data regarding the magnitude of the amplitude of the motion ofthe object of evaluation may be a proportion of a region in which avalue of a product of a normalized amplitude and a normalized dispersionof the amplitudes is higher than or equal to a predetermined thresholdwith respect to an entire image of the object of evaluation.

The index data regarding the frequency per unit time of the peak of themotion of the object of evaluation may be an average value of productsof a normalized number of the peaks per unit time and a normalizeddispersion of the numbers of the peaks per unit time over an entirescreen.

The index data regarding the frequency per unit time of the peak of themotion of the object of evaluation may be a proportion of a region inwhich a value of a product of a normalized number of the peaks per unittime and a normalized dispersion of the numbers of the peaks per unittime is higher than or equal to a predetermined threshold with respectto an entire image of the object of evaluation.

The index data generating unit may further generate index data regardinga classification result of classifying each of partial regions of theimage of the object of evaluation on the basis of the feature amount ofthe motion of the object of evaluation, and the evaluation valuecalculating unit may further calculate an evaluation value thatevaluates the classification result of the feature amount of the motionof the object of evaluation using the index data regarding theclassification result generated by the index data generating unit.

The index data generating unit may calculate a motion amount of theobject of evaluation detected by the motion detecting unit, and theevaluation value calculating unit may generate an image of a change inthe motion amount calculated by the index data generating unit withrespect to time so as to be displayed.

The index data generating unit may generate index data that represents achange due to administration of a drug to a cardiomyocyte in a peak of awaveform representing a relaxation of the cardiomyocyte which is theobject of evaluation, in the change in the calculated motion amount withrespect to time, and the evaluation value calculating unit may evaluatethe index data calculated by the index data generating unit andcalculates an evaluation value.

An imaging unit which images the object of evaluation and obtains animage of the object of evaluation may further be included, and themotion detecting unit may detect the motion of the object of evaluationusing the image of the object of evaluation obtained by the imagingunit.

The motion detecting unit may detect the motion of the object ofevaluation between frame images in an evaluation duration with apredetermined length in the image of the object of evaluation which is amoving image.

The motion detecting unit may repeat detection of the motion of theobject of evaluation in the evaluation duration a predetermined numberof times.

The evaluation value calculating unit may evaluate each of a pluralityof kinds of the index data generated by the index data generating unitto calculate the evaluation value, and integrate the calculatedevaluation values, thereby calculating an evaluation value thatevaluates the object of evaluation.

The object of evaluation may be a cell that moves spontaneously.

The object of evaluation may be a cultured cell generated by culturing acell collected from a living body.

According to another aspect of the present disclosure, in addition, animage processing method includes: detecting a motion of an object ofevaluation using an image of the object of evaluation by a motiondetecting unit of an image processing apparatus; by using a motionvector that represents the detected motion of the object of evaluation,generating index data that represents a feature of the motion of theobject of evaluation and is used as an index for evaluating the objectof evaluation by an index data generating unit of the image processingapparatus; and evaluating the generated index data and calculating anevaluation value by an evaluation value calculating unit of the imageprocessing apparatus.

According to another aspect of the present disclosure, moreover, aprogram causes a computer to function as: a motion detecting unit whichdetects a motion of an object of evaluation using an image of the objectof evaluation; an index data generating unit which, by using a motionvector that represents the detected motion of the object of evaluation,generates index data that represents a feature of the motion of theobject of evaluation and is used as an index for evaluating the objectof evaluation; and an evaluation value calculating unit which evaluatesthe generated index data and calculates an evaluation value.

According to another aspect of the present disclosure, the motion of theobject of evaluation is detected by using the image of the object ofevaluation, the motion vector that represents the detected motion of theobject of evaluation is used, the index data that represents the featureof the motion of the object of evaluation and is used as the index forevaluating the object of evaluation is generated, the generated indexdata is evaluated, and the evaluation value is calculated.

Advantageous Effects of Invention

According to the present disclosure, there is an excellent effect ofobtaining classification data that becomes an index for enablingaccurate evaluation of an object that performs a periodic motion withhigh accuracy.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 illustrates a configuration example of a cultured cardiomyocyteevaluation system 100.

FIG. 2 is a block diagram illustrating a configuration example of anevaluation index data generating device 300.

FIG. 3 is a diagram schematically illustrating a structure example ofevaluation object image data 600.

FIG. 4 is a block diagram illustrating a configuration example of amotion detecting unit 310.

FIG. 5 is a diagram schematically illustrating a process of dividingframe image data 610 into blocks 611.

FIG. 6 is a diagram schematically illustrating a structure example ofmotion detection data 700.

FIG. 7 is a diagram illustrating examples of calculated feature amounts.

FIG. 8 is a diagram of an example of angle values set for respectivetemplates in correspondence with average motion directions, which areone of the feature amounts calculated according to a first embodiment.

FIG. 9 is a diagram schematically illustrating a structure example ofevaluation index data 800.

FIG. 10 is a flowchart showing an example of the order of a processperformed by the evaluation index data generating device 300 accordingto the first embodiment.

FIG. 11 is a flowchart showing an example of the order of a processperformed by the evaluation index data generating device 300 accordingto the second embodiment.

FIG. 12 is a diagram for explaining the summary of other evaluationmethods of cells.

FIG. 13 is a block diagram illustrating a main configuration example ofa cultured cardiomyocyte evaluation apparatus.

FIG. 14 is a block diagram illustrating a main configuration example ofa evaluation index data generating unit of FIG. 13.

FIG. 15 is a diagram for explaining a form of data stored in a motionfeature amount data history storage memory.

FIG. 16 is a block diagram illustrating a main configuration example ofan evaluation unit of FIG. 13.

FIG. 17 is a diagram for explaining an example of amplitude evaluation.

FIG. 18 is a diagram for explaining an example of amplitude evaluation.

FIG. 19 is a diagram for explaining an example of pulsation evaluation.

FIG. 20 is a diagram for explaining an example of pulsation evaluation.

FIG. 21 is a flowchart for explaining an example of the flow of anevaluation process.

FIG. 22 is a flowchart for explaining an example of the flow of aevaluation index data generation process.

FIG. 23 is a flowchart for explaining an example of the flow of a motionevaluation process.

FIG. 24 is a block diagram illustrating a main configuration example ofa drug evaluation apparatus.

FIG. 25 is a diagram for explaining an example of a form of a change inmotion amount due to a pulsation with respect to time.

FIG. 26 is a block diagram illustrating a main configuration example ofan evaluation index data generating unit.

FIG. 27 is a block diagram illustrating a main configuration example ofan evaluation unit.

FIG. 28 is a flowchart for explaining an example of the flow of anevaluation process.

FIG. 29 is a flowchart for explaining an example of the flow of anevaluation index data generation process.

FIG. 30 is a flowchart for explaining an example of the flow of animpact evaluation process.

FIG. 31 is a diagram showing an example of forms of changes in pulsationrhythm due to drug administration.

FIG. 32 is a diagram for explaining forms of variations of pulsationbehaviors due to drug administration.

FIG. 33 is a block diagram illustrating a main configuration example ofa personal computer.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments for embodying a present technique (hereinafter,referred to as embodiments) will be described. Descriptions will beprovided in the following order.

1. First Embodiment (Evaluation Index Data Generation Process: Exampleof Performing Classification Process Using Templates)

2. Second Embodiment (Evaluation Index Data Generation Process: Exampleof Performing Classification Process Using k-means Method)

3. Third Embodiment (Cultured Cardiomyocyte Evaluation Apparatus) 4.Fourth Embodiment (Drug Evaluation Apparatus) 5. Fifth Embodiment(Personal Computer) 1. First Embodiment [Configuration Example ofCultured Cardiomyocyte Evaluation System]

FIG. 1 illustrates a configuration example of a cultured cardiomyocyteevaluation system 100. The cultured cardiomyocyte evaluation system 100illustrated in this figure is for evaluating the quality of culturedcardiomyocytes 500.

In regenerative medicine, treatment of various tissues, organs, and thelike of the human body is performed using cells cultured in vitro. Thecultured cardiomyocytes 500 are cultured as cells provided for suchtreatment for heart disease. Currently, there is a situation in which atechnique for mass-producing such cultured cells and supplying asufficient amount of cells in medical practice at low cost has beendeveloped. In a case of achieving the situation in which cultured cellsare mass-produced as such, efficiently and accurately evaluating thecultured cells is required.

The cultured cardiomyocytes 500 themselves perform motions correspondingto pulsations. The quality of the cultured cardiomyocytes 500 may bedetermined by evaluating whether or not the motions corresponding to thepulsations are favorable. On the basis of this, the culturedcardiomyocyte evaluation system 100 records moving image data having thephotographed cultured cardiomyocytes 500 and performs evaluation on thebasis of motion detection results of the recorded moving image data.Accordingly, more detailed and accurate evaluation results than those ofnoninvasive and visual evaluation are obtained.

As a configuration for this, the cultured cardiomyocyte evaluationsystem 100 includes, for example, as illustrated in the figure, animaging device 110, an evaluation object image data generating andrecording device 200, an evaluation index data generating device 300,and an evaluation device 400.

The imaging device 110 is for photographing the cultured cardiomyocytes500 which are an object of evaluation. In addition, in this figure, astate where the cultured cardiomyocytes 500 are directly photographed bythe imaging device 110 is illustrated. However, in practice, forexample, a microscope image of the cultured cardiomyocytes 500 is imagedin the configuration. In addition, during the imaging, the imagingposition of the imaging device 110 with respect to the culturedcardiomyocytes 500 is in a fixed state.

The evaluation object image data generating and recording device 200 isa device for generating evaluation object image data on the basis of animage signal input from the imaging device 110 and recording and savingthe generated evaluation object image data in, for example, a recordingmedium therein.

The evaluation object image data generated here becomes, for example,moving image data generated from the image signal of the imaged culturedcardiomyocytes 500.

The evaluation index data generating device 300 is, for example, adevice which receives the moving image data saved as the evaluationobject image data in the evaluation object image data generating andrecording device 200 and generates evaluation index data used as anindex for evaluation of the cultured cardiomyocytes 500. The evaluationdevice 400 is a device which obtains evaluation results by processingthe evaluation index data generated by the evaluation index datagenerating device 300.

[Configuration Example of Evaluation Index Data Generating Device]

FIG. 2 illustrates a configuration example of the evaluation index datagenerating device 300. The evaluation index data generating device 300illustrated in this figure includes a motion detecting unit 310, amotion detection data storage unit 320, a feature amount calculatingunit 330, and a classification processing unit 340. In addition,evaluation object image data 600 illustrated in this figure is obtainedby reproducing data recorded in the evaluation object image datagenerating and recording device 200, and is moving image data includingframe image data as described above.

The motion detecting unit 310 is a unit which receives evaluation objectimage data 600 and performs a motion detection process. In addition, aspecific example of the motion detection process by the motion detectingunit 310 in this case and a structure example of motion detection datawill be described later. In addition, the motion detection data storageunit 320 is a unit which stores the motion detection data obtained asdetection results by the motion detection process of the motiondetecting unit 310.

The feature amount calculating unit 330 is a unit which calculates andacquires a predetermined feature amount using the motion detection datastored in the motion detection data storage unit 320. In addition, anexample of the feature amount calculated here will be described later.

The classification processing unit 340 is a unit for obtainingevaluation index data 800 by performing a classification process on thebasis of the information on the feature amount obtained by the featureamount calculating unit 330. A specific example of the classificationprocess will be described later. The evaluation index data 800 obtainedby the classification processing unit 340 is an example ofclassification data described in the claims.

[Structure of Evaluation Object Image Data]

FIG. 3 illustrates a structure example of the evaluation object imagedata 600 input to the evaluation index data generating device 300. Asillustrated in this figure, the evaluation object image data 600includes first to (T+1)-th frame image data 610-1 to (T+1) correspondingto a predetermined time.

In addition, the moving image data saved as the evaluation object imagedata 600 in the evaluation object image data generating and recordingdevice 200 may include the frame image data 610-1 to T illustrated inFIG. 3 as it is. In addition, moving image data including sections ofthe frame image data 610-1 to T may also be employed. In the lattercase, for example, image sections that are determined to be optimal forevaluation are extracted from the moving image data saved as theevaluation object image data 600 in the evaluation object image datagenerating and recording device 200. In addition, moving image data asthe image sections may be input to the evaluation index data generatingdevice 300 as the evaluation object image data 600 of FIG. 3.

[Configuration Example of Motion Detecting Unit]

FIG. 4 illustrates a configuration example of the motion detecting unit310. The motion detecting unit 310 illustrated in this figure includes aframe memory 311 and a motion vector calculating unit 312. The framememory 311 is a unit which holds the frame image data 610 which aresequentially input for each frame duration as the evaluation objectimage data 600.

The motion vector calculating unit 312 is a unit which calculates motionvectors. Therefore, the motion vector calculating unit 312 receivesframe image data which is input as the evaluation object image data 600of a current time and frame image data of the previous time held in theframe memory 311. In addition, motion vectors are calculated using thetwo frame image data. The calculated motion vectors are held in themotion detection data storage unit 320 as motion detection data 700.

Next, a process performed by the motion detecting unit 310 including theunits illustrated in FIG. 4 will be described. The motion vectorcalculating unit 312 as described above receives the frame image data610 of the current time and the frame image data 610 of the previoustime. The motion vector calculating unit 312 divides the received frameimage data 610 in units of blocks. That is, as illustrated in FIG. 5, atwo-dimensional pixel region formed by the frame image data 610 isdivided into M blocks in the horizontal direction and is divided into Nblocks in the vertical direction. As a result, the frame image data 610is divided into (M×N) blocks 611. Each of the blocks 611 includes, forexample, (16×16) pixels. The motion vector calculating unit 312 in thiscase calculates motion vectors as the motion detection process in unitsof the blocks 611 as objects of the process. In addition, the motiondetection process is performed sequentially using the first to (T+1-thframe image data 610.

In addition, the motion detection data 700 obtained in a stage in whichthe final motion detection process using the T-th and (T+1)-th frameimage data 610 is completed is as illustrated in FIG. 6. First, themotion detection data 700 illustrated in this figure includes T frameunit motion detection data 710-1 to T. Each of the frame unit motiondetection data 710-1 to T is obtained by performing the motion detectionprocess on the frame image data 610 of the current time and the previousframe image data 610 obtained for each frame duration as an object. Forexample, the third frame unit motion detection data 710-1 is obtained byinputting third frame image data 610-4 and third frame image data 610-3as the frame image data of the current time and the previous time andperforming motion detection thereon.

Each of the frame-corresponding motion detection data 710-1 to T isformed of (M×N) block unit motion detection data 711. Each of the blockunit motion detection data 711 corresponds to the corresponding singleblock 611 and becomes data that represents a motion vector detected forthe corresponding block 611. As such, the motion detection data 700 hasa structure in which each of the frame-corresponding motion detectiondata 710 has (M×N) block unit motion detection data 711. This means thattime-series data for a motion vector corresponding to each of the blocks611 that form the frame image data 610 is obtained.

[Examples of Feature Amount Calculated by Feature Amount CalculatingUnit]

The feature amount calculating unit 330 calculates a plurality offeature amounts using the motion detection data 700 stored in the motiondetection data storage unit 320. First, examples of the feature amountscalculated and acquired by the feature amount calculating unit 330 willbe described with reference to FIG. 7.

FIG. 7 illustrates motion vectors that are represented by the block unitmotion detection data 711 corresponding to a certain single block 611 intime series. That is, the number of the block unit motion detection data711 corresponding to the single block 611 is also T so as to correspondto the fact that the number of frame-corresponding motion detection data710 is T as described in FIG. 6. FIG. 7 samples the motion vectorsrepresented by the T block unit motion detection data 711 in atime-series order.

In addition, the block unit motion detection data 711 has a motionamount of a horizontal direction component and a motion amount of avertical direction component as information of the motion vectors, andin FIG. 7, the motion amount of any one of the horizontal directioncomponent and the vertical direction component is illustrated. In FIG.7, the vertical axis represents the motion amount, and the horizontalaxis represents the frames, that is, the time.

In addition, in the following description, as illustrated in FIG. 7, asection corresponding to a predetermined time obtained as the T blockunit motion detection data 711 (that is, data of the motion amounts) aredeveloped in the time-series direction is referred to as an “evaluationsection”.

As the examples of the feature amounts that may be calculated, here, anaverage motion amount Vav, an average motion direction θav, an averageamplitude Aav, an average acceleration Bav, an average pulsationinterval Dav, and a pulsation starting time S are assumed. Any of suchfeature amounts is obtained on the basis of the motion vectors asunderstood by the following description. In addition, such featureamounts are obtained for each of the blocks 611.

First, the average motion amount Vav will be described. As illustratedin FIG. 7, a change in the motion amount with respect to time is showndepending on the T block unit motion detection data 711. This isobtained corresponding to a periodic change between a state where thecultured cardiomyocytes 500 move and a state where they stop in responseto the pulsations generated in the cultured cardiomyocytes 500. In thispoint, for example, it can be said that the change in the motion amountwith respect to time has features corresponding to pulsations. Here, inthe present disclosure, the average of T motion amounts obtained in theevaluation section is treated as a feature amount. The average motionamount Vav may be obtained by the following expression assuming that themotion amounts of the horizontal direction component and the verticaldirection component obtained from the motion vectors in each of theblock unit motion detection data 711 are Vx and Vy and a variablecorresponding to a frame order is n.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 1} \right\rbrack & \; \\{{V = \sqrt{V_{x}^{2} + V_{y}^{2}}},{{Vav} = {\frac{1}{T}{\sum\limits_{n = 1}^{T}V_{n}}}}} & \;\end{matrix}$

That is, as the average motion amount Vav, first, a synthesized motionamount V is obtained by synthesizing the motion amounts Vx and Vy of thehorizontal direction component and the vertical direction component foreach of the T block unit motion detection data 711. In addition, theaverage motion amount Vav is obtained by calculating the average valueof the T synthesized motion amounts V.

Otherwise, first, the average value (horizontal average motion amountVavx) of the motion amounts Vx of the horizontal direction component andthe average value (vertical average motion amount Vavy) of the motionamounts Vy of the vertical direction component in the evaluation periodare calculated. Next, the average motion amount Vav may also becalculated by synthesizing the horizontal average motion amount Vavx andthe vertical average motion amount Vavy.

For example, the higher the value of the average motion amount Vav is,the greater the motion corresponding to the pulsation of a part of thecultured cardiomyocytes 500 corresponding to the block 611 may beevaluated to be.

Next, the average motion direction θav is the average value of T motiondirections θ obtained in the evaluation section. The average motiondirection θav may be obtained by the following expression.

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 2} \right\rbrack & \; \\{{\theta = {\cos^{- 1}\frac{V_{y}}{V_{x}}}},{{\theta \; {av}} = {\frac{1}{T}{\sum\limits_{n = 1}^{T}\theta_{n}}}}} & \;\end{matrix}$

For example, by the distribution of the average motion directions θav ofthe corresponding blocks 611 in the entire frame image, the uniformityof the directions in which the cultured cardiomyocytes 500 are moved dueto the pulsations may be evaluated. In addition, the distribution statethat a part having non-uniform motion directions is in, and the like mayalso be evaluated.

For example, according to a case where there are actual movements as themotions of the pulsations of the cultured cardiomyocytes 500, asillustrated in FIG. 7, an amplitude of higher than or equal to apredetermined value periodically occurs. The average amplitude Aav isthe average value of amplitudes of higher than or equal to thepredetermined value obtained as described above in the evaluationsection. The average amplitude Aav may be obtained, for example, asfollows.

First, peak detection is performed on the synthesized motion amounts Vwhich are K motion vectors in the evaluation section, and the detectedpeak values are averaged, thereby obtaining the average amplitude Aav.Otherwise, first, peak detection is performed on the motion amounts ofthe horizontal direction component, and the detected peak values areaveraged, thereby obtaining an average amplitude (horizontal averageamplitude) Aavx of the horizontal direction component. In the samemanner, an average amplitude (vertical average amplitude) Aavy of thevertical direction component is obtained. Next, an operation ofcalculating the average amplitude Aav by performing an operation ofsynthesizing the horizontal average amplitude Aavx and the verticalaverage amplitude Aavy may be considered. For example, the greater theaverage amplitude Aav is, the greater the motion corresponding to thepulsation of the part of the cultured cardiomyocytes 500 correspondingto the block 611 may be evaluated to be.

In addition, as illustrated in FIG. 7, it can be said that the change inmotion amount in time series also represents a change in accelerationwith respect to the motions by pulsations. The average acceleration Bavis the average value of accelerations obtained in the evaluation sectionand may be obtained, for example, as follows.

First, the synthesized motion amounts B represented by a predeterminednumber of block unit motion detection data 711 which are sequential inthe time-series order are differentiated. This is performed on the firstto T-th block unit motion detection data 711, thereby calculating anacceleration B for each predetermined time in the evaluation section. Inaddition, the average acceleration Bav is obtained as the average valueof the accelerations B. Otherwise, first, the motion amounts Vx of thehorizontal direction component represented by a predetermined number ofblock unit motion detection data 711 which are sequential in thetime-series order are sequentially differentiated to calculate ahorizontal acceleration Bx for each predetermined time, and a horizontalaverage acceleration Bavx is obtained as the average value of thehorizontal accelerations Bx. In the same manner, a vertical averageacceleration Bavy is calculated. In addition, the average accelerationBav may also be obtained by synthesizing the horizontal averageacceleration Bavx and the vertical average acceleration Bavy. Forexample, the average acceleration Bav becomes an index of rapidity whenthe cultured cardiomyocytes 500 are changed from a still state to amoving state in response to pulsations. When the average accelerationBav is high, the motions of the corresponding part of the culturedcardiomyocytes 500 in response to the pulsations may be evaluated to beactive to that extent.

In addition, as also described before, the peak of an amplitudeperiodically appears in response to pulsations. In FIG. 7, a timeinterval at which the peak of the amplitude appears is represented as apulsation interval. The average pulsation interval Dav is the averagevalue of pulsation intervals obtained in the evaluation section and maybe obtained, for example, as follows.

First, peak detection is performed in the same manner as when theaverage amplitude Aav is obtained. In addition, a frame timing at whichthe peak is detected, that is, a time is obtained. Next, a time widthfrom a time at which a single peak is detected to a time at which thenext peak is detected is calculated as a pulsation interval D. Theaverage pulsation interval Dav is obtained by calculating the averagevalue of the calculated pulsation intervals D. For example, by thedistribution of the average pulsation intervals Dav of the correspondingblocks 611 in the entire frame, the uniformity of the pulsation timeintervals in the entire cultured cardiomyocytes 500 may be evaluated. Inaddition, in a case of focusing on a non-uniform distribution, thedistribution state that the shift of the pulsation time interval is inmay be evaluated. The average pulsation interval Dav is an example of anaverage motion interval described in the claims.

In addition, the pulsation starting time S is obtained by measuring atime until the peak of an amplitude of a motion amount corresponding tothe motion of the initial pulsation after starting the evaluationsection is obtained. Even regarding the pulsation starting time S, forexample, by the distribution of the pulsation starting times S of thecorresponding blocks 611 in the entire frame, the uniformity of thepulsation starting times in the entire cultured cardiomyocytes 500 maybe evaluated. In addition, in a case of focusing on a non-uniformdistribution, the distribution state that the shift of the pulsationstarting timing is in may be evaluated.

In this manner, any of the feature amounts is calculated on the basis ofthe detected motion vectors (motion amounts). That is, in the presentdisclosure, it is possible to quantify various items from thetime-series data of the motion vectors.

The feature amount calculating unit 330 is able to be configured tocalculate any of the six feature amounts including the average motionamount Vav, the average motion direction θav, the average amplitude Aav,the average acceleration Bav, the average pulsation interval Dav, andthe pulsation starting time S are assumed. For example, in practice, thefeature amounts needed to perform the classification process performedby the classification processing unit 340 may be calculated from suchfeature amounts.

[Example of Classification Process]

The classification processing unit 340 performs the classificationprocess using a plurality of kinds of feature amounts calculated by thefeature amount calculating unit 330 as described above, and obtains theclassification process results as the evaluation index data 800. Severalmethods may be considered as such classification methods, and here, amethod called clustering is employed. That is, a plurality ofclassification categories called clusters are set, and each of theblocks 611 that form the frame image data 610 illustrated in FIG. 5 isclassified to any of the plurality of clusters according to the featureamount thereof.

As a specific example of the classification process by clustering, atemplate method is employed in the first embodiment. In addition, as theemployed feature amounts, there are two feature amounts including theaverage motion amount Vav and the average motion direction θav fromamong those assumed in advance. Accordingly, the feature amountcalculating unit 330 calculates the average motion amount Vav and theaverage motion direction θav.

The classification processing unit 340 in this case has, for example,the following first to fifth templates in combinations of the averagemotion amount Vav and the average motion direction θav. In addition,FIG. 8 is referred to describe such templates. FIG. 8 illustrates aspecific example of the average motion directions θ specified in thefirst to fifth templates described as follows.

First, the first temperature is shown by the following expression.

(Vav,θav)=(0,0)

In the above expression, (Vav,θav) represents a combination of theaverage motion amount Vav and the average motion direction θav. Inaddition, the average motion amount Vav in the above expression is “0”,and this means that the motion amount over the evaluation section is 0.That is, the first template has a state in which an image partcorresponding to the position of the block 611 is stopped in theevaluation section as a template.

In addition, the second template is shown by the following expression.

(Vav,θav)=(a,0)

In the above expression, a may have an arbitrary value other than 0 asthe average motion amount Vav. That is, the second template has acombination of the motion of the image part corresponding to theposition of the block 611 and a direction of “0” shown in FIG. 8 as theaverage motion direction θav, as a template.

In addition, the third template is shown by the following expression.

(Vav,θav)=(a,π/4)

That is, the third template has a combination of the motion of the imagepart corresponding to the position of the block 611 and a direction of“π/4(45°)” as the average motion direction θav, as a template.

In addition, the fourth template is shown by the following expression.

(Vav,θav)=(a,π/2)

That is, the fourth template has a combination of the motion of theimage part corresponding to the position of the block 611 and adirection of “π/2(90°)” shown in FIG. 8 as the average motion directionθav, as a template.

In addition, the fifth template is shown by the following expression.

(Vav,θav)=(a,3π/4)

That is, the fifth template has a motion state in which the motion ofthe image part corresponding to the position of the block 611 is presentand the average motion direction θ is a direction of 3π/4(135°) shown inFIG. 8 as a template.

As the first to fifth templates are prepared, 5 clusters correspondingto the respective templates are present. Here, clusters respectivelycorresponding to the first to fifth templates are called the first tofifth clusters.

The classification processing unit 340 calculates the combination(Vav,θav) of the feature amounts obtained for the single block 611 andthe distance between the first to fifth templates. In addition, theblock 611 is classified into a cluster corresponding to the templateclosest to the calculated distance. For example, when the calculateddistance is closest to the third template, the block 611 is classifiedinto the third cluster. The classification process is performed on eachof the blocks 611. As a result, data having content in which each of theblocks 611 that form the frame image data 610 is classified to any ofthe first to fifth clusters is obtained. This becomes the evaluationindex data 800 obtained by the classification process in the firstembodiment.

FIG. 9 schematically illustrates the evaluation index data 800 obtainedby the classification process in the first embodiment. As illustrated inthis figure, the evaluation index data 800 includes a group of (M×N)individual classification result data 801. The individual classificationresult data 801 are matched with the blocks 611 that form the frameimage data 610 through one-to-one correspondence, and have informationrepresenting which one of the first to fifth clusters is the clusterinto which the corresponding block 611 is classified. In this figure,which one of the first to fifth clusters is the block 611 correspondingto the individual classification result data 801 classified into isshown by the numbers 1 to 5 shown in the individual classificationresult data 801.

In addition, in FIG. 9, as the evaluation index data 800, a structure inwhich the individual classification result data 801 are arranged in an(M×N) matrix. Each of the individual classification result data 801arranged in this manner corresponds to the block 611 disposed at thesame position in the frame image data 610.

For example, it can be said that the distribution of the first to fifthclusters in a single frame image is shown in the evaluation index data800 having the structure shown in FIG. 9. It is ascertained that thisinformation indicates, in all the imaged cultured cardiomyocytes 500,which parts have a motion or which parts do not have a motion, and whichdirection the moving parts move in. More specifically, the motionregarding the pulsation of the cultured cardiomyocytes 500 may beascertained as follows.

For example, first, the cultured cardiomyocytes 500 need to havemotions, and cultured cardiomyocytes 500 in which a large part thereofdoes not have motions are evaluated as poor quality. In the evaluationindex data 800 shown in FIG. 9, this may be evaluated by the number ofindividual classification result data 801 classified into the firstcluster.

In addition, although there are motions due to the same pulsations,cultured cardiomyocytes 500 have higher quality as they move in the samedirection as much as possible. For example, regarding the evaluationindex data 800 shown in FIG. 9, the occupation ratios of thecorresponding clusters are plotted as a histogram to evaluate the ratiosof the directions of the motions due to the pulsations in the entirecultured cardiomyocytes 500. Moreover, in a case of different motiondirections, not only the ratios of the directions of the motions, butalso the distribution state that such a part is in may be accuratelyknown by the distribution of the clusters shown in FIG. 9.

In the example of the evaluation index data 800 of FIG. 9, in thearrangement of the individual classification result data 801corresponding to the frame image data 610, the individual classificationresult data 801 classified into the third cluster are distributed in awide region at the center. This represents that the culturedcardiomyocytes 500 as the object of evaluation have a tendency to movein the direction of (π/4(45°)) shown in FIG. 8. For example, in a casewhere an evaluator wants to check such a distribution state of motiondirections, an image classified according to the clusters into which theblocks 611 corresponding to the individual classification result data801 based on FIG. 9 are classified is generated and displayed by theevaluation device 400. In addition, by checking this image, theevaluator may accurately perceive the state that the directions of themotion of the cultured cardiomyocytes 500 are in.

In this manner, in the first embodiment, on the basis of the two featureamounts including the average motion amount Vav and the average motiondirection θav, the presence of a part having motion and the presence ofa part without motion in the cultured cardiomyocytes 500 may bedistinguished and recognized as described above. In addition, regardingthe part having motion, the degree to which the directions of the motionare uniform may be recognized. In addition, the distribution of the partwithout motion and a part with different directions of motion in thepart with motion may also be recognized. That is, in relation to thepulsations of the cultured cardiomyocytes 500, more accurate anddetailed evaluation may be performed on two points including thepresence or absence of the motion and the motion directions. Inaddition, this means that by combining a plurality of feature amounts,evaluation regarding a plurality of evaluation items for each of thefeature amounts is possible.

Here, the present technique does not need to be limited to thecombination of the average motion amount Vav and the average motiondirection θav in the above description. That is, a combination of one ormore arbitrary feature amounts selected from the 6 feature amountsexemplified in advance may be selected. In addition, in a case where aplurality of feature amounts is selected, various evaluation items areobtained according to the selected combination.

For example, the evaluation device 400 in this case recognizes the stateof each part as described above by inputting the evaluation index data800 as shown in FIG. 9 and performing a process using a predeterminedalgorithm, and outputs the recognition results in a form that is able tobe perceived by the evaluator. For example, when the clusterclassification result as shown in FIG. 9 is expressed as an image so asto be displayed and output, the evaluator may visually perceive thefact.

[Example of Process Order of Evaluation Index Data Generating Device]

The flowchart of FIG. 10 shows an example of the order of a processperformed by the evaluation index data generating device 300 in thefirst embodiment. In addition, the process of each of the steps in thisfigure is appropriately performed by any of the motion detecting unit310, the feature amount calculating unit 330, and the classificationprocessing unit 340 illustrated in FIG. 2. At least a part of theprocess of each of the steps shown in FIG. 10 may be configured by beingrealized by the CPU (Central Processing Unit) of a computer device thatexecutes programs.

The process from Steps S901 to S907 in FIG. 10 is a motion detectionprocess performed by the motion detecting unit 310. First, the motiondetecting unit 310 substitutes a variable n corresponding to numbersgiven to the frame image data 610 that form the evaluation object imagedata 600 with 2 as initial settings (Step S901). Next, the motion vectorcalculating unit 312 in the motion detecting unit 310 receives (n−1)-thframe image data and n-th frame image data (Step S902). That is, theprevious frame image data held in the frame memory 311 and the currentframe image data are input. Next, the motion vector calculating unit 312performs a process of dividing each of the input frame image data intoblocks having a predetermined pixel count (Step S903). In addition, themotion detection process is performed by a method such as block matching(Step S904).

According to the motion detection process in Step S904, a single frameunit motion detection data 710-(n−1) in the motion detection data 700shown in FIG. 6 is obtained. Here, the motion detecting unit 310 storesthe frame unit motion detection data 710-(n−1) in the motion detectiondata storage unit 320 (Step S905).

Next, the motion detecting unit 310 increments the variable n (StepS906) and determines whether or not the variable n is greater than themaximum value (T+1) (Step S907). In addition, the maximum value (T+1)corresponds to the number of frame image data that form the evaluationobject image data 600. In a case where the determination result that thevariable n is greater than the maximum value (T+1) is obtained (StepS907), the process from Step S902 is repeatedly performed. Accordingly,the first frame unit motion detection data 710-1 to the T-th frame unitmotion detection data 710-T are sequentially stored in the motiondetection data storage unit 320. In addition, when a stage in which theT-th frame unit motion detection data 710-T is stored is reached, it isdetermined that the variable n is greater than the maximum value T (StepS907), and the process proceeds to the order after Step S908.

In Step S907, as the variable n is determined to be greater than (T+1),the feature amount calculating unit 330 performs a process ofcalculating feature amounts using the motion detection data 700 (StepS908). The feature amounts calculated here are, for example, the averagemotion amount Vav and the average motion direction θav as describedabove.

Subsequently, the classification process according to the templatemethod described above is performed by the classification processingunit 340. Therefore, first, the classification processing unit 340substitutes a variable i representing numbers given to the (M×N) blocks611 that form the frame image data 610 with 1 (Step S909). Next, theclassification processing unit 340 calculates the distance between thefeature amount (Vav,θav) calculated for the i-th block 611 and thefeature amount (Vav,θav) of each of a plurality of templates prepared inadvance (Step S910). The feature amounts (Vav,θav) represents thecombination of the average motion amount Vav and the average motiondirection θav described above.

In addition, the classification processing unit 340 classifies the i-thblock 611 into the cluster corresponding to the template from which thecalculated distance is shortest. In addition, the individualclassification result data 801 representing the classification resultsis generated (Step S911). The individual classification result data 801has information of content corresponding to an identifier of the clusterclassified into an identifier of the i-th block. In addition, in theexample described above, as the first to fifth templates are prepared, 5clusters including the first to fifth clusters are prepared. Regardingthe correspondence in this example, in Step S911, the block 611 as anobject of the classification process is classified to any one cluster ofthe first to fifth clusters.

Next, the classification processing unit 340 increments the variable i(Step S912) and determines whether or not the variable i is greater thanthe maximum value (M×N) (Step S913). Here, in a case where the variablei is less than or equal to the maximum value (M×N), the process isreturned to Step S910, and the process of sequentially classifying theblocks into the clusters is repeated. In addition, when the clusterclassification of all the (M×N) blocks 611 is completed, it isdetermined that the variable i is greater than the maximum value (M×N)in Step S913. According to the determination result, the classificationprocessing unit 340 generates and outputs the evaluation index data 800by the individual classification result data obtained by the process ofSteps S910 to S912 until now (Step S914).

In addition, the combination of the average motion amount Vav and theaverage motion direction θav in the above description is an example. Inthe first embodiment, for example, the evaluation index data 800 may begenerated by a combination of one or more arbitrary feature amountsselected from the 6 feature amounts exemplified in advance. In addition,on the basis of the calculation method described above, the averagemotion amount Vav may also be obtained by separately obtaining thehorizontal average motion amount Vavx and the vertical average motionamount Vavy. The average amplitude Aav is obtained by separatelyobtaining the horizontal average amplitude Aavx and the vertical averageamplitude Aavy. The average acceleration Bav is obtained by separatelyobtaining the horizontal average acceleration Bavx and the verticalaverage acceleration Bavy. Here, for example, it may be thought that thefeature amounts including the horizontal direction components andvertical direction components are treated independently so as to be usedto generate the evaluation index data 800. In addition, in the abovedescription, the number of clusters is 5 and may also be set to anothernumber.

2. Second Embodiment [Configuration of Evaluation Index Data GeneratingDevice]

The classification process in the first embodiment uses templates, andother methods may be considered as the method of the classificationprocess. Here, as a second embodiment, a configuration that employsanother classification process method will be described.

The configuration of the evaluation index data generating device 300corresponding to the second embodiment is, for example, as in FIG. 2.However, the number of kinds of feature amounts calculated by thefeature amount calculating unit 330 and the order of the classificationprocess performed by the classification processing unit 340 aredifferent as in the following description.

[Example of Process Order of Evaluation Index Data Generating Device]

The flowchart of FIG. 11 shows an example of the order of a processperformed by the evaluation index data generating device 300corresponding to the second embodiment. In this figure, the process fromStep S901 to S907 is the same as that in FIG. 10 corresponding to thefirst embodiment described before.

When it is determined that the variable n is greater than the maximumvalue T in Step S907, the feature amount calculating unit 330 calculatesfeature amounts for each of the blocks 611 using the motion detectiondata 700 stored in the motion detection data storage unit 320 (StepS908A).

As the feature amounts calculated for each of the blocks 611 in StepS908A, there are 9 amounts as follows. That is, the feature amountsinclude a horizontal average motion amount Vavx, a vertical averagemotion amount Vavy, an average motion direction θav, a horizontalaverage amplitude Aavx, a vertical average amplitude Aavy, a horizontalaverage acceleration Bavx, a vertical average acceleration Bavy, anaverage pulsation interval Dav, and a pulsation starting time S.

The classification processing unit 340 performs clustering based on ak-means method (k-means method) as follows by using the feature amountscalculated as described above. That is, the classification processingunit 340 calculates a 9-dimensional vector x which combines the 9feature amounts for each of the blocks 611 (Step S921).

In Step S921, (M×N) vectors x corresponding to the blocks 611 areobtained. Then, the classification processing unit 340 in this casefirst performs initial cluster classification (initial classification)on the (M×N) vectors xi (1≤i≤(M×N)) according to the k-means method.That is, K samples corresponding to the number K of clusters set inadvance are extracted from the vector xi and the distances between thesamples and the vector xi other than the samples are calculated. Inaddition, the vector xi other than the samples is classified into thesame cluster as that of the sample having the closest calculateddistance.

Next, the classification processing unit 340 calculates the center ofgravity Gj (1≤j≤K) for each of the first to K-th clusters according tothe final classification results until now (S923). The center of gravityGj varies depending on the final classification results. Next, theclassification processing unit 340 calculates the distance between eachof the clusters and the center of gravity Gj for each of the vectors xi(Step S924). In addition, re-classification is performed by classifyingeach of the vectors xi into the cluster having the shortest calculateddistance (Step S925). The process from Steps S923 to S925 is repeateduntil the classification result has no change and becomes the same asthat of the previous result in Step S926.

In addition, the classification result determined to be the same as thatof the previous result according to Step S926 becomes, that is, thefinal classification result. Here, the classification processing unit340 generates and outputs evaluation index data 800 from theclassification result that is finally obtained (Step S914A). That is, inthe final classification result, the vector xi is classified into any ofthe clusters. Here, the classification processing unit 340 generates,for example, individual classification result data 801 in which theidentifier of the block 611 corresponding to the vector xi correspondsto the identifier of the classified cluster. In addition, a group of theindividual classification result data 801 for each of the i-th to M×N-thblocks 611 is generated as the evaluation index data 800.

As understood from the above description, the evaluation index data 800obtained in the second embodiment also has the structure shown in, forexample, FIG. 9. In addition, each of the clusters represents acombination of numerical ranges of a plurality of different featureamounts. Therefore, each of the clusters has different meaning inrelation to the periodic motion as pulsations. Therefore, using theevaluation index data 800 obtained in the second embodiment, accurateand detailed evaluation results may also be obtained.

In addition, the present disclosure shows an example for embodying thepresent technique, and as clearly described in the present disclosure,items in the present disclosure have a correspondence relationship withspecific items of the invention in the claims. Similarly, the specificitems of the invention in the claims have a correspondence relationshipwith the items of the present disclosure denoted by the same names.However, the present technique is not limited to the embodiments and maybe embodied by making various modifications of the embodiments in arange without departing from the gist of the present technique.

For example, as the feature amounts used for generating the evaluationindex data 800, combinations other than those specifically described ineach of the embodiments may also be employed. In addition, featureamounts other than those specifically described in each of theembodiments may be obtained and combined. In addition, from among thefeature amounts obtained in each of the embodiments, any of the motionamount, motion direction, amplitude, acceleration, pulsation interval,and the like is an average value of the values obtained for each of theframe periods. However, such feature amounts have changes in timeseries. Here, it may be considered that, for example, changes in timeseries are calculated as feature amounts to be used to generate theevaluation index data 800. In addition, as the classification processperformed by the classification processing unit 340, other algorithmsand methods may also be employed. In addition, the object of evaluationis the cultured cardiomyocytes 500. However, for example, theconfiguration of the present disclosure may be applied to other objectsas long as the motions thereof have periodicity.

In addition, the process order described in the present disclosure maybe ascertained as a method having a series or orders and may also beascertained as a program for executing such a series of orders on acomputer and a recording medium that stores the program. As therecording medium, for example, CD (Compact Disc), MD (MiniDisc), DVD(Digital Versatile Disc), memory card, Blu-ray Disc (Blu-ray Disc(registered trademark)), and the like may be used.

3. Third Embodiment [Overview of Another Example of Evaluation Method]

The evaluation method of cells may be a method other that that describedabove. For example, an evaluation value may be obtained for the indexcalculated from the motion vector obtained for each of the blocks ofcultured cells.

For example, when cultured cells 1001 as shown A in FIG. 12 are anobject of evaluation, first, as shown B in FIG. 12, a motion vector 1002may be obtained for each block (partial region) every predetermined time(for example, each frame), a change in the motion amount of each blockwith respect to time may be obtained as in a graph 1003 shown C in FIG.12, from this data, data that represents changes in the amplitude andthe number of pulsations of the motions of the cells with respect totime as in a graph 1004 shown D in FIG. 12 may be generated, andevaluation values for evaluating such indexes may be obtained, therebyevaluating the motions of the cells using the evaluation values.

In this manner, using the evaluation values, the object of evaluation(for example, the motions of cells) may be evaluated morequantitatively. In addition, since the motion vector is used to generatethe indexes, more various indexes may be obtained easily and in anoninvasive manner. That is, the object of evaluation (for example, themotions of cells) may be more correctly evaluated.

[Cultured Cardiomyocyte Evaluation Apparatus]

FIG. 13 is a block diagram illustrating a main configuration example ofa cultured cardiomyocyte evaluation apparatus.

The cultured cardiomyocyte evaluation apparatus 1100 illustrated in FIG.13 is an apparatus that performs evaluation of motions of culturedcardiomyocytes 500, like the cultured cardiomyocyte evaluation system100 of FIG. 1. That is, the cultured cardiomyocyte evaluation apparatus1100 realizes the cultured cardiomyocyte evaluation system 100 as asingle apparatus. As such, the configuration of the culturedcardiomyocyte evaluation system 100 is arbitrary as long as the functionof the entire system is not changed. For example, the plurality ofdevices illustrated in FIG. 1 may be configured as a single device, anda single device may be configured as a plurality of devices. Forexample, the entirety of the cultured cardiomyocyte evaluation system100 may be configured as a single apparatus as illustrated in FIG. 13.

In other words, even in this embodiment, like the cultured cardiomyocyteevaluation system 100 of the first embodiment and the second embodimentfor example, the cultured cardiomyocyte evaluation apparatus 1100 mayalso be configured of a plurality of devices. Hereinafter, descriptionwill be provided using the cultured cardiomyocyte evaluation apparatus1100.

However, in the case of this embodiment, unlike the first embodiment andthe second embodiment, (the motions of) the cultured cardiomyocytes 500may be evaluated by a method other than the evaluation method describedabove. That is, as described above, the cultured cardiomyocyteevaluation apparatus 1100 of this embodiment obtains evaluation valuesthat evaluate the motions of an object of evaluation.

The cultured cardiomyocytes 500 illustrated in FIG. 13 are livingtissues (cell group) for heart disease, which are generated by culturingcardiomyocytes collected from a living body in vitro. Cardiomyocyteshave pulsations by always repeating contraction and relaxation. Whensuch cardiomyocytes are cultured and grown to the culturedcardiomyocytes 500, ideally, the operations of the cells are related toeach other and the entirety of the cultured cardiomyocytes 500 pulsatesas a single living tissue.

The cultured cardiomyocyte evaluation apparatus 1100 has, for example,the cultured cardiomyocytes 500 cultured as described above as an objectof evaluation and evaluates the motions thereof in order to evaluate theperformance of the cultured cardiomyocytes 500.

In addition, the object of evaluation of the cultured cardiomyocyteevaluation apparatus 1100 may be other than the cultured cardiomyocytes500. For example, cultured cells other than cardiomyocytes may be anobject of evaluation. As a matter of course, the object of evaluationmay be other than cells. However, as the object of evaluation, an objectthat moves by itself and is able to be evaluated by evaluating themotions thereof is desirable. In addition, the motions may be achievedautonomously (spontaneously) like cardiomyocytes, and may also beachieved by an electric signal supplied from the outside, and the like.

As illustrated in FIG. 13, the cultured cardiomyocyte evaluationapparatus 1100 includes an imaging unit 1101, an evaluation object imagedata generating and recording unit 1102, an evaluation index datagenerating unit 1103, and an evaluation unit 1104.

The imaging unit 1101 corresponds to the imaging devices 110 of FIG. 1.That is, the imaging unit 1101 images the cultured cardiomyocytes 500which are the object of evaluation. In addition, the imaging unit 1101may directly image the cultured cardiomyocytes 500 (without usinganother member), and may image the cultured cardiomyocytes 500 usinganother member such as a microscope. In addition, the culturedcardiomyocytes 500 may be fixed to the imaging unit 1101 or may also notbe fixed. Since the cultured cardiomyocyte evaluation apparatus 1100detects motions (temporal changes in position), generally, it isdesirable that the cultured cardiomyocytes 500 be fixed to the imagingunit 1101.

The imaging unit 1101 supplies an image signal of the image of thecultured cardiomyocytes 500 obtained through imaging to the evaluationobject image data generating and recording unit 1102.

The evaluation object image data generating and recording unit 1102corresponds to the evaluation object image data generating and recordingdevice 200 of FIG. 1. That is, the evaluation object image datagenerating and recording unit 1102 generates evaluation object imagedata on the basis of the image signal supplied from the imaging unit1101 and records and saves the generated evaluation object image datain, for example, a recording medium therein. The evaluation object imagedata generated here becomes, for example, moving image data generatedfrom the image signal of the imaged cultured cardiomyocytes 500.

For example, the evaluation object image data generating and recordingunit 1102 may extract frame images only in a partial period from aplurality of frame images supplied from the imaging unit 1101 and usethese as the evaluation object image data. In addition, for example, theevaluation object image data generating and recording unit 1102 mayextract a partial region of each of the frame images supplied from theimaging unit 1101 as a small frame image and use a moving image made ofthe small frame images as the evaluation object image data. Moreover,for example, the evaluation object image data generating and recordingunit 1102 may perform arbitrary image processing on each of the frameimages supplied from the imaging unit 1101 and use the image processingresult as the evaluation object image data. As the image processing, forexample, image enlargement, reduction, rotation, deformation, luminanceor chromaticity correction, sharpness, noise removal, intermediate frameimage generation, and the like may be considered. As a matter of course,any image processing other than those may be employed.

The evaluation object image data generating and recording unit 1102supplies the stored evaluation object image data to the evaluation indexdata generating unit 1103 at a predetermined timing.

The evaluation index data generating unit 1103 corresponds to theevaluation index data generating device 300 of FIG. 1. That is, theevaluation index data generating unit 1103 performs motion detection ofthe object of evaluation (the cultured cardiomyocytes 500) for each ofthe blocks which are partial regions into which the entire region of theimage of the object of evaluation (cultured cardiomyocytes 500) isdivided, between the frame images of the supplied evaluation objectimage data. The evaluation index data generating unit 1103 expresses thedetected motion of each of the blocks as a motion vector and obtainsvarious feature amounts (motion feature amount data) regarding themotions of the object of evaluation (cultured cardiomyocytes 500) fromthe motion vector. In addition, as described in the first embodiment andthe second embodiment, the evaluation index data generating unit 1103classifies the blocks on the basis of the motion feature amount data.

The evaluation index data generating unit 1103 supplies the motionfeature amount data generated as described above and the classificationresult to the evaluation unit 1104 as evaluation index data.

The evaluation unit 1104 corresponds to the evaluation device 400 ofFIG. 1. That is, the evaluation unit 1104 calculates an evaluation valuefor each of the supplied evaluation index data, integrates thecalculated evaluation values, and obtains the evaluation values of theobject of evaluation (cultured cardiomyocytes 500).

[Evaluation Index Data Generating Unit]

FIG. 14 is a block diagram illustrating a main configuration example ofthe evaluation index data generating unit 1103. As illustrated in FIG.14, the evaluation index data generating unit 1103 has, like theevaluation index data generating device 300 of FIG. 2, the motiondetecting unit 310 and the motion detection data storage unit 320. Inaddition, the evaluation index data generating unit 1103 has a featureamount calculating unit 1123 instead of the feature amount calculatingunit 330 of the evaluation index data generating device 300 of FIG. 2and has a classification processing unit 1124 instead of theclassification processing unit 340 of the evaluation index datagenerating device 300 of FIG. 2. Moreover, the evaluation index datagenerating unit 1103 has a motion feature amount data history storagememory 1125.

The motion detecting unit 310 receives the evaluation object image data600, performs motion detection thereon, and supplies the detectionresult (motion vector) as motion detection data to the motion detectiondata storage unit 320 so as to be stored. As described with reference toFIGS. 3 to 6, the motion detecting unit 310 includes the frame memory311 and the motion vector calculating unit 312, divides the entireregion of each of the frame images of the evaluation object image data600 into M×N (M and N are arbitrary natural numbers) blocks, andperforms motion detection on each of the blocks using a method such asblock matching between frame images, thereby generating a motion vector.

As illustrated in FIG. 7, the motion detecting unit 310 performs motiondetection in an evaluation section (for example, T+1 frames (T is anarbitrary natural number)) having a predetermined length. For example,as illustrated in FIG. 6, the motion detecting unit 310 generates(M×N×T) motion detection data (motion vectors) using (T+1) frame imagesand stores the resultant in the motion detection data storage unit 320.

When motion detection in a single evaluation section is ended (when(M×N×T) motion detection data (motion vectors) are stored in the motiondetection data storage unit 320), the feature amount calculating unit1123 acquires the motion detection data and calculates feature amountsregarding the motions of the cultured cardiomyocytes 500 from the motiondetection data.

For example, the feature amount calculating unit 1123 calculates featureamounts regarding the motions (pulsations) of the culturedcardiomyocytes 500 for each of the blocks, using the (M×N×T) motiondetection data (motion vectors).

For example, the feature amount calculating unit 1123 calculates theaverage value (average amplitude Aav) of the amplitudes of the motionsof the cultured cardiomyocytes 500 in the evaluation section exemplifiedin FIG. 7 as one of the feature amounts regarding the motions of thecultured cardiomyocytes 500.

As shown in FIG. 7, the amplitude is an amplitude when the motion amountis changed. The average amplitude Aav is the average value of theamplitudes in the corresponding evaluation section. The amplitude A andthe average amplitude Aav are calculated for each of the blocks.

That is, when the horizontal direction component of each amplitude is Axand the vertical direction component thereof is Ay, each amplitude A iscalculated by the following Expression (1).

[Math. 3]

A=√{square root over (A _(X) ² +A _(Y) ²)}   (1)

When a variable corresponding to a frame order is n, using eachamplitude An calculated by Expression (1), the average amplitude Aav inthe evaluation section is calculated by the following Expression (2).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 4} \right\rbrack & \; \\{{Aav} = {\frac{1}{T}{\sum\limits_{n = 1}^{T}A_{n}}}} & (2)\end{matrix}$

The feature amount calculating unit 1123 calculates the averageamplitude Aav for each of the blocks.

In addition, for example, the feature amount calculating unit 1123calculates the average value (average pulsation interval Dav) of thepulsation intervals (or the number of pulsations per unit time) of themotions of the cultured cardiomyocytes 500 in one evaluation sectionexemplified in FIG. 7 as one of the feature amounts regarding themotions of the cultured cardiomyocytes 500.

The pulsation interval D is an interval of peaks of the motion amountsas shown in FIG. 7. The average pulsation interval Dav is the averagevalue of the pulsation intervals D in the corresponding evaluationsection. The feature amount calculating unit 1123 calculates such anaverage pulsation interval Day for each of the blocks.

That is, when a variable corresponding to a frame order is n and atiming of the peak of the motion amount is Pn, the pulsation interval Dnis calculated by the following Expression (3).

[Math. 5]

Dn=P(n+1)−P(n)  (3)

Therefore, the average pulsation interval Day in the evaluation sectionis calculated by the following Expression (4).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 6} \right\rbrack & \; \\{{Dav} = {\frac{1}{T}{\sum\limits_{n = 1}^{T}{Dn}}}} & (4)\end{matrix}$

The feature amount calculating unit 1123 calculates such an averagepulsation interval Dav for each of the blocks.

That is, in the case of the above example, two kinds of feature amountsare generated for each of the (M×N) blocks. The kinds and the number offeature amounts calculated by the feature amount calculating unit 1123are arbitrary. For example, as described in the first embodiment, theaverage motion amount Vav, the average motion direction θav, the averageacceleration Bav, and the pulsation starting time S may be calculated asthe feature amounts.

In addition, a method of calculating each of the feature amounts isarbitrary. For example, in the case of the feature amounts such as theaverage motion amount Vav, the average amplitude Aav, and the averageacceleration Bav which have both the horizontal component and thevertical component, the feature amount calculating unit 1123 maycalculate the average of each of the components and synthesize theaverages of both the components.

The feature amount calculating unit 1123 supplies the calculated featureamounts to the motion feature amount data history storage memory 1125 asthe motion feature amount data so as to be stored. As a matter ofcourse, the feature amount calculating unit 1123 may sequentially supplythe obtained feature amounts to the motion feature amount data historystorage memory 1125 as the motion feature amount data so as to bestored. In addition, the feature amount calculating unit 1123 may causea part of the obtained feature amounts to be stored in the motionfeature amount data history storage memory 1125 as the motion featureamount data.

In addition, the feature amount calculating unit 1123 also supplies thecalculated feature amounts to the classification processing unit 1124.

Like the classification processing unit 340 of FIG. 2, theclassification processing unit 1124 performs a classification processusing the plurality of kinds of feature amounts calculated by thefeature amount calculating unit 1123 and supplies the classificationprocess results to the motion feature amount data history storage memory1125 as the motion feature amount data so as to be stored.

The evaluation index data generating unit 1103 repeats the generation ofevaluation index data as described above S times. That is, the imagingunit 1101 continues imaging and generates frame images for at least atime of (evaluation section (T+1 frames)×S times), and the evaluationobject image data generating and recording unit 1102 generates theevaluation object image data for at least (evaluation section×S times).In addition, in the evaluation object image data, the evaluationsections may not be continuous over time.

For example, it is assumed that a duration from the start of the cultureto the end of the culture is 10 days and T=600 frames are imaged every 2hours to perform evaluation. In this case, each evaluation section is600 frames, and the evaluation section is repeated S=120 times.

The evaluation index data generating unit 1103 generates feature amountsfor each of blocks in each of the evaluation sections as describedabove. Accordingly, in the motion feature amount data history storagememory 1125, M×N×S feature amounts are stored as illustrated in FIG. 15.In addition, in a case where a plurality of kinds of feature amounts aregenerated, more feature amounts (M×N×S×the number of kinds) are storedin the motion feature amount data history storage memory 1125.

In this manner, the generation of the feature amounts is repeated Stimes, and when a predetermined number of feature amounts are stored,the motion feature amount data history storage memory 1125 supplies thestored feature amounts to the evaluation unit 1104 as the evaluationindex data 800 at a predetermined timing.

[Evaluation Unit]

FIG. 16 is a block diagram illustrating a main configuration example ofthe evaluation unit 1104. As illustrated in FIG. 16, the evaluation unit1104 has an evaluation unit (an evaluation unit for each of the indexes)for each of the supplied evaluation index data 800. In the example ofFIG. 16, the evaluation unit 1104 has, as the evaluation units for theindexes, an amplitude evaluation unit 1141, a number of pulsationsevaluation unit 1142, and a classification result evaluation unit 1143.

The amplitude evaluation unit 1141 evaluates the average amplitude Aavsupplied as the evaluation index data. The number of pulsationsevaluation unit 1142 evaluates the average pulsation interval Davsupplied as the evaluation index data. The classification resultevaluation unit 1143 evaluates the classification process resultsupplied as the evaluation index data.

The evaluation units of the indexes represent the kinds of index datathat are able to be evaluated by the evaluation unit 1104. Basically,the evaluation unit 1104 is set to be able to evaluate all the suppliedevaluation index data. Therefore, for example, in a case where anotherevaluation index data 800 is supplied to the evaluation unit 1104, anevaluation unit corresponding to the evaluation index data 800 isprepared in the evaluation unit 1104. As such, the kinds and the numberof evaluation units for the indexes included in the evaluation unit 1104are dependent on the kinds and the number of supplied evaluation indexdata.

Operation Example 1 of Amplitude Evaluation Unit

Next, a specific example of amplitude evaluation by the amplitudeevaluation unit 1141 will be described. In general, it is desirable thatthe amplitude of the pulsations of the cardiomyocytes be significantlystable. Here, the amplitude evaluation unit 1141 calculates anevaluation value to have a greater value in a case where the amplitudeis more significantly stabilized.

In this case, the amplitude evaluation unit 1141 first normalizes eachamplitude A (average amplitude Aav) which is the evaluation index datafor each of the blocks of the frame images as in the followingExpression (5) using such a function fa as a curve 1161 of the graphshown in FIG. 17A (obtains an amplitude A′ normalized by the functionfa).

[Math. 7]

A′=f _(a)(A)  (5)

For example, when it is assumed that the number of blocks in the entireframe image is M×N and the calculation of the average amplitude Aav isrepeated S times, the amplitude evaluation unit 1141 normalizes each ofthe M×N×S average amplitudes Aav using the function fa.

The function fa may be any function as long as the function produces agreater value as the value of the amplitude A is greater and produces asmaller value as the value thereof is smaller. That is, the normalizedamplitude A′ takes a greater value as the amplitudes are greater andtakes a smaller value as the amplitudes are smaller.

Next, the amplitude evaluation unit 1141 obtains a dispersion Va of theamplitudes of the past N times for each of the blocks as in thefollowing Expression (6).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 8} \right\rbrack & \; \\{V_{a} = {\frac{1}{N}{\sum\limits_{k = 0}^{N - 1}\left( {\overset{\_}{A} - {A\left( {t - k} \right)}} \right)^{2}}}} & (6)\end{matrix}$

In addition, in Expression (6), the overlined A is the average value ofthe amplitudes A (average amplitude Aav). In addition, in the case wherethe calculation of the average amplitude Aav is repeated S times, N=S isachieved. That is, for example, when it is assumed that the number ofblocks in the entire frame image is M×N and the calculation of theaverage amplitude Aav is repeated S times, the amplitude evaluation unit1141 calculates M×N dispersions Va from the M×N×S average amplitudesAav.

Next, the amplitude evaluation unit 1141 normalizes the dispersion Va ofthe amplitudes as in the following Expression (7) using such a functionga as a curve 1162 of the graph shown in FIG. 17B (obtains a dispersionVa′ normalized by the function ga).

[Math. 9]

V′ _(a) =g _(a)(V _(a))  (7)

For example, when it is assumed that the number of blocks in the entireframe image is M×N, the amplitude evaluation unit 1141 normalizes eachof the M×N dispersions Va using the function ga.

The function ga may be any function as long as the function produces asmaller value as the value of the dispersion Va is greater and producesa greater value as the value thereof is smaller. That is, the normalizeddispersion Va′ of the amplitudes takes a greater value with smallervariations and takes a smaller value with greater variations.

Next, the amplitude evaluation unit 1141 calculates the average value(M×N average values) over the entire screen of the products of thenormalized amplitude A′ and the normalized dispersion Va′ of theamplitudes as an evaluation value Ea as in the following Expression (8).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 10} \right\rbrack & \; \\{E_{a} = {\frac{1}{MN}{\sum\limits_{k = 0}^{M - 1}{\sum\limits_{l = 0}^{N - 1}{A_{k,l}^{\prime}V_{k,l}^{\prime}}}}}} & (8)\end{matrix}$

In this case, the evaluation value Ea has a greater value as thenormalized amplitude and the normalized dispersion of the amplitudes inthe entire frame image are greater. That is, a case where the amplitudeof each of the blocks is greater and is more stabilized (the amplitudeis greater and variations in the time direction thereof are smaller) isevaluated at a higher degree.

In addition, the amplitude evaluation unit 1141 may also calculate theproportion of the number Na1 of blocks in which the value of the productof the normalized amplitude A′ and the normalized dispersion Va′ of theamplitudes is greater than or equal to a predetermined threshold Ta1with respect to the entire frame image as an evaluation value Ea as inthe following Expression (9).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 11} \right\rbrack & \; \\{E_{a} = \frac{N_{a\; 1}}{MN}} & (9)\end{matrix}$

The threshold Ta1 is an arbitrary value set in advance. When the valueis set to be greater, the evaluation reference is increased (evaluationconditions become strict), and the value of the evaluation value Ea isreduced. In this case, the evaluation value Ea takes a higher value asthe number of blocks in which the product of the amplitude and thedispersion in the entire frame image is stabilized to be greater than apredetermined reference is greater.

That is, in this case, it is preferable that variations between blocksbe smaller compared to a case where the evaluation value Ea iscalculated using the average value as described above. For example, inthe case of evaluating the average value, there may be cases whereevaluation is enhanced although variations between blocks are great.Contrary to this, in a case where evaluation is performed using athreshold, even though the value of a partial block is excessively high,a high degree of evaluation is not achieved when the number Na1 ofblocks is not great.

Operation Example 2 of Amplitude Evaluation Unit

In addition, the amplitude evaluation method is not limited to theabove-described example. For example, the pulsations of the culturedcardiomyocytes may be compared to those in a case of an ideal and normalculture and the comparison results may be evaluated. In this case, atransition pattern of the pulsations during ideal and normal culture(ideal transition pattern) is determined in advance.

As shown in the graph A of FIG. 18, the amplitude evaluation unit 1141compares a transition pattern of the pulsations of the culturedcardiomyocytes (measured transition pattern) to the ideal transitionpattern and evaluates the similarity therebetween. In A of FIG. 18, afull line 1171 represents the ideal transition pattern of amplitudes,and a dotted line 1172 represents the measured transition pattern of theamplitudes. As the difference between the two is smaller, the evaluationvalue is greater.

First, the amplitude evaluation unit 1141 calculates the sum Da of thedistances between the two transition patterns at each elapsed time foreach of the blocks as in the following Expression (10).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 12} \right\rbrack & \; \\{D_{a} = {\sum\limits_{k = 0}^{S - 1}{{W_{a}(k)}\sqrt{\left( {{A(k)} - {A_{I}(k)}} \right)^{2}}}}} & (10)\end{matrix}$

In Expression (10), A(k) is the amplitude A (average amplitude Aav) inthe measured transition pattern, and A_(I)(k) is the amplitude A(average amplitude Aav) in the ideal transition pattern. k representswhich number of times is the measurement value (elapsed time) (in a casewhere the measurement is repeated S times, 0≤k≤S−1). In addition,W_(a)(k) is a weight coefficient, and the value thereof is arbitrary.For example, the difference between the two transition patterns is notimportant immediately after the start of the measurement. However, in acase where the two transition patterns are required to approximate eachother as the elapsed time is increased, the value of the weightcoefficient W_(a) is set to be greater as the value of k is greater.

As described above, when the sum Da of the distances between the twotransition patterns at each elapsed time is obtained, the amplitudeevaluation unit 1141 then normalizes the sum Da of the distances usingsuch a function ha as a full line 1173 of the graph shown in FIG. 18B asin the following Expression (11) (calculates a normalized sum Da′ of thedistances).

[Math. 13]

D′ _(a) =h _(a)(D _(a))  (11)

The function ha may be any function as long as the function produces asmaller value as the value of the sum Da of the distances is greater andproduces a greater value as the value thereof is smaller. That is, thenormalized sum Da′ of the distances takes a greater value as thedifference between the ideal transition pattern and the measuredtransition pattern is smaller and takes a smaller value as thedifference between the ideal transition pattern and the measuredtransition pattern is greater.

Next, the amplitude evaluation unit 1141 calculates the average value(M×N average values) over the entire screen of the normalized sums Da′of the distances as an evaluation value Ea as in the followingExpression (12).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 14} \right\rbrack & \; \\{E_{a} = {\frac{1}{MN}{\sum\limits_{k = 0}^{M - 1}{\sum\limits_{l = 0}^{N - 1}D_{k,l}^{\prime}}}}} & (12)\end{matrix}$

In this case, the evaluation value Ea has a greater value as thedifference between the measured transition and the ideal transition inthe entire frame image is smaller.

In addition, the amplitude evaluation unit 1141 may also calculate theproportion of the number Na2 of blocks in which the value of thenormalized sum Da′ of the distances is greater than or equal to apredetermined threshold Ta2 with respect to the entire frame image as anevaluation value Ea as in the following Expression (13).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 15} \right\rbrack & \; \\{E_{a} = \frac{N_{a\; 2}}{MN}} & (13)\end{matrix}$

In Expression (13), the threshold Ta2 is an arbitrary value set inadvance. When the value is set to be greater, the evaluation referenceis increased (evaluation conditions become strict), and the value of theevaluation value Ea is reduced. In this case, the evaluation value Eatakes a higher value as the number of blocks, in which the differencebetween the measured transition and the ideal transition in the entireframe image is stabilized to be smaller than a predetermined reference,is greater.

In this manner, the amplitude evaluation unit 1141 calculates theevaluation value Ea that evaluates the amplitudes on the basis of theindex data regarding the amplitudes of the pulsations of thecardiomyocytes. That is, the amplitude evaluation unit 1141 mayquantitatively perform evaluation on the amplitudes of the pulsations ofthe cardiomyocytes.

Operation Example 1 of Number of Pulsations Evaluation Unit

Next, a specific example of evaluation of number of pulsations by thenumber of pulsations evaluation unit 1142 will be described. In general,it is desirable that regarding the pulsations of the cardiomyocytes, thenumber of pulsations per unit time (rate) be stabilized as anappropriate value. Here, the number of pulsations evaluation unit 1142calculates an evaluation value to have a greater value in a case wherethe number of pulsations per unit time is stabilized as an appropriatevalue.

In this case, the number of pulsations evaluation unit 1142 firstcalculates the number R of pulsations per unit time (for example, for 1minute) from the pulsation intervals D (average pulsation interval Dav)as in the following Expression (14).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 16} \right\rbrack & \; \\{{R\lbrack{bps}\rbrack} = \frac{60}{D\left\lbrack \sec \right\rbrack}} & (14)\end{matrix}$

That is, the number R of pulsations per unit time is the average valueof the numbers of pulsations per unit time in the evaluation section(for example, (T+1) frames). The number of pulsations evaluation unit1142 calculates the number R of pulsations per unit time for each of theblocks. In addition, the number of pulsations evaluation unit 1142calculates the number R of pulsations per unit time for each ofevaluation durations. That is, when it is assumed that the number ofblocks in a single frame image is M×N and the evaluation duration isrepeated S times, the number of pulsations evaluation unit 1142calculates (M×N×S) numbers R of pulsations per unit time.

Next, the number of pulsations evaluation unit 1142 normalizes thenumbers R of pulsations per unit time using such a function fr as acurve 1181 of the graph shown A of in FIG. 19 as in the followingExpression (15) (obtains the number R′ of pulsations per unit timenormalized by the function fr).

[Math. 17]

R′=f _(r)(R)  (15)

For example, when it is assumed that the number of blocks in the entireframe image is M×N and the calculation of the pulsation interval Dav isrepeated S times, the number of pulsations evaluation unit 1142normalizes each of the M×N×S numbers R of pulsations per unit time usingthe function fr. The function fr may be any function as long as thefunction produces a greater value as the value of the number R ofpulsations per unit time is closer to an appropriate value and producesa smaller value as the value thereof is further from the appropriatevalue. That is, the normalized number R′ of the pulsations per unit timetakes a greater value as is closer to an appropriate number ofpulsations per unit time determined in advance and takes a smaller valueas is further from the appropriate number of pulsations per unit timedetermined in advance.

Next, the number of pulsations evaluation unit 1142 obtains a dispersionVr per unit time the past N times for each of the blocks as in thefollowing Expression (16).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 18} \right\rbrack & \; \\{V_{r} = {\frac{1}{N}{\sum\limits_{k = 0}^{N - 1}\left( {\overset{\_}{R} - {R\left( {t - k} \right)}} \right)^{2}}}} & (16)\end{matrix}$

In addition, in Expression (16), the overlined R is the average value ofthe numbers R of pulsations per unit time. In addition, as describedabove, in the case where the calculation of the number R of pulsationsper unit time is repeated S times, N=S is achieved. That is, forexample, when it is assumed that the number of blocks in the entireframe image is M×N and the calculation of the number R of pulsations perunit time is repeated S times, the number of pulsations evaluation unit1142 calculates M×N dispersions Vr from the M×N×S numbers R ofpulsations per unit time.

Next, the number of pulsations evaluation unit 1142 normalizes thedispersion Vr of the amplitudes as in the following Expression (17)using such a function gr as a curve 1182 of the graph shown in FIG. 19B(obtains a dispersion Vr′ of the number of pulsations per unit timenormalized by the function gr).

[Math. 19]

V′ _(r) =g _(r)(V _(r))  (17)

For example, when it is assumed that the number of blocks in the entireframe image is M×N, the number of pulsations evaluation unit 1142normalizes each of the M×N dispersions Vr using the function gr.

The function gr may be any function as long as the function produces asmaller value as the value of the dispersion Vr is greater and producesa greater value as the value thereof is smaller. That is, the normalizeddispersion Vr′ of the numbers of pulsations per unit time takes agreater value with smaller variations and takes a smaller value withgreater variations.

Next, the number of pulsations evaluation unit 1142 calculates theaverage value (M×N average values) over the entire screen of theproducts of the normalized number R′ of pulsations per unit time and thenormalized dispersion Vr′ of the numbers of pulsations per unit time asan evaluation value Er as in the following Expression (18).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 20} \right\rbrack & \; \\{E_{r} = {\frac{1}{MN}{\sum\limits_{k = 0}^{M - 1}{\sum\limits_{l = 0}^{N - 1}{R_{k,l}^{\prime}V_{k,l}^{\prime}}}}}} & (18)\end{matrix}$

In this case, the evaluation value Er has a greater value as thenormalized number of pulsations per unit time and the normalizeddispersion in the entire frame image are greater. That is, a case wherethe normalized number R of pulsations per unit time of each of theblocks is more stabilized (the number of pulsations per unit time iscloser to an appropriate value and variations in the time directionthereof are smaller) is evaluated at a higher degree.

In addition, the number of pulsations evaluation unit 1142 may alsocalculate the proportion of the number Nr1 of blocks in which the valueof the product of the normalized number R′ of pulsations per unit timeand the normalized dispersion Vr′ of the numbers of pulsations per unittime is greater than or equal to a predetermined threshold Tr1 withrespect to the entire frame image as an evaluation value Er as in thefollowing Expression (19).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 21} \right\rbrack & \; \\{E_{r} = \frac{N_{r\; 1}}{MN}} & (19)\end{matrix}$

The threshold Tr1 is an arbitrary value set in advance. When the valueis set to be greater, the evaluation reference is increased (evaluationconditions become strict), and the value of the evaluation value Er isreduced. In this case, the evaluation value Er takes a higher value asthe number of blocks in which the number of pulsations per unit time inthe entire frame image is closer to an appropriate value than apredetermined reference and is stabilized in the time direction isgreater.

That is, in this case, it is preferable that variations between blocksbe smaller compared to a case where the evaluation value Er iscalculated using the average value as described above. For example, inthe case of evaluating the average value, there may be cases whereevaluation is enhanced although variations between blocks are large.Contrary to this, in a case where evaluation is performed using athreshold, even though the value of a partial block is excessively high,a high degree of evaluation is not achieved when the number Nr1 ofblocks is not great.

Operation Example 2 of Number of Pulsations Evaluation Unit

In addition, the method of evaluating the number of pulsations per unittime is not limited to the above-described example. For example, thepulsations of the cultured cardiomyocytes may be compared to those in acase of ideal and normal culture and the comparison results may beevaluated. In this case, a transition pattern of the pulsations duringideal and normal culture (ideal transition pattern) is determined inadvance.

As shown in the graph A of FIG. 20, the number of pulsations evaluationunit 1142 compares a transition pattern of the pulsations of thecultured cardiomyocytes (measured transition pattern) to the idealtransition pattern and evaluates the similarity therebetween. In A ofFIG. 20, a full line 1191 represents the ideal transition pattern of thenumbers of pulsations per unit time, and a dotted line 1192 representsthe measured transition pattern of the numbers of pulsations per unittime. As the difference between the two is smaller, the evaluation valueis greater.

First, the number of pulsations evaluation unit 1142 calculates the sumDr of the distances between the two transition patterns at each elapsedtime for each of the blocks as in the following Expression (20).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 22} \right\rbrack & \; \\{D_{r} = {\sum\limits_{k = 0}^{S - 1}{{W_{r}(k)}\sqrt{\left( {{R(k)} - {R(k)}} \right)^{2}}}}} & (20)\end{matrix}$

In Expression (20), R(k) is the number R of pulsations per unit time inthe measured transition pattern, and R_(I)(k) is the number ofpulsations per unit time in the ideal transition pattern. k representswhich number of times is the measurement value (elapsed time) (in a casewhere the measurement is repeated S times, 0≤k≤S−1). In addition,W_(r)(k) is a weight coefficient, and the value thereof is arbitrary.For example, the difference between the two transition patterns is notimportant immediately after the start of the measurement. However, in acase where the two transition patterns are required to approximate eachother as the elapsed time is increased, the value of the weightcoefficient W_(r) is set to be greater as the value of k is greater.

As described above, when the sum Dr of the distances between the twotransition patterns at each elapsed time is obtained, the number ofpulsations evaluation unit 1142 then normalizes the sum Dr of thedistances using such a function hr as a full line 1193 of the graphshown in FIG. 20B as in the following Expression (21) (calculates anormalized sum Dr′ of the distances).

[Math. 23]

D′ _(r) =h _(r)(D _(r))  (21)

The function hr may be any function as long as the function produces asmaller value as the value of the sum Dr of the distances is greater andproduces a greater value as the value thereof is smaller. That is, thenormalized sum Dr′ of the distances takes a greater value as thedifference between the ideal transition pattern and the measuredtransition pattern is smaller and takes a smaller value as thedifference between the ideal transition pattern and the measuredtransition pattern is greater.

Next, the number of pulsations evaluation unit 1142 calculates theaverage value (M×N average values) over the entire screen of thenormalized sums Dr′ of the distances as an evaluation value Er as in thefollowing Expression (22).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 24} \right\rbrack & \; \\{E_{r} = {\frac{1}{MN}{\sum\limits_{k = 0}^{M - 1}{\sum\limits_{l = 0}^{N - 1}D_{k,l}^{\prime}}}}} & (22)\end{matrix}$

In this case, the evaluation value Er has a greater value as thedifference between the measured transition and the ideal transition inthe entire frame image is smaller.

In addition, the number of pulsations evaluation unit 1142 may alsocalculate the proportion of the number Nr2 of blocks in which the valueof the normalized sum Dr′ of the distances is greater than or equal to apredetermined threshold Tr2 with respect to the entire frame image as anevaluation value Er as in the following Expression (23).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 25} \right\rbrack & \; \\{E_{r} = \frac{N_{r\; 2}}{MN}} & (23)\end{matrix}$

In Expression (23), the threshold Tr2 is an arbitrary value set inadvance. When the value is set to be greater, the evaluation referenceis increased (evaluation conditions become strict), and the value of theevaluation value Er is reduced. In this case, the evaluation value Ertakes a higher value as the number of blocks, in which the differencebetween the measured transition and the ideal transition in the entireframe image is stabilized to be smaller than a predetermined reference,is greater.

In this manner, the number of pulsations evaluation unit 1142 calculatesthe evaluation value Er that evaluates the number of pulsations per unittime on the basis of the index data regarding the number of pulsationsper unit time of the pulsations of the cardiomyocytes. That is, thenumber of pulsations evaluation unit 1142 may quantitatively performevaluation on the number of pulsations per unit time of the pulsationsof the cardiomyocytes.

[Evaluation Example of Classification Result Evaluation unit]

Next, a specific example of evaluation of cluster classification resultsby the classification result evaluation unit 1143 will be described. Ingeneral, it is desirable that, regarding the pulsations of thecardiomyocytes, the proportion of blocks classified into desirableclusters be increased. Here, the classification result evaluation unit1143 calculates a greater evaluation value in a case where theproportion of blocks classified into a predetermined cluster (desirablecluster) of which feature amounts are in a desirable state is greater.

For example, it is assumed that the desirable cluster is C. Theclassification result evaluation unit 1143 first counts the number oftimes at which classification into C is made during the classificationperformed the past n times for each of the blocks, compares the number Nof times to a predetermined threshold Tc1 determined in advance, andobtains the number Nc of blocks that satisfy the following conditionalexpression (24).

[Math. 26]

N>T _(c1)  (24)

The classification result evaluation unit 1143 calculates an evaluationvalue Ec that evaluates the classification results using the number Ncof blocks obtained in this manner as in the following Expression (25)(the number of blocks of a single frame image is assumed to be N×N).

$\begin{matrix}\left\lbrack {{Math}.\mspace{14mu} 27} \right\rbrack & \; \\{E_{c} = \frac{N_{c}}{MN}} & (25)\end{matrix}$

As described above, the classification result evaluation unit 1143calculates the evaluation value Ec that evaluates the classificationresults of the feature amounts of the pulsations of the cardiomyocytes.That is, the classification result evaluation unit 1143 mayquantitatively perform evaluation on the classification results of thefeature amounts of the pulsations of the cardiomyocytes.

[Evaluation Integrating Unit]

Returning to FIG. 16, the evaluation unit 1104 further includes anevaluation integrating unit 1144. Each of the evaluation units for therespective indexes of the evaluation unit 1104 supplies the evaluationvalue for the corresponding calculated index to the evaluationintegrating unit 1144.

The evaluation integrating unit 1144 integrates the evaluation valuessupplied from the evaluation units for the respective indexes through apredetermined operations and generates an evaluation value E of theobject of evaluation (cultured cardiomyocytes 500). For example, theevaluation integrating unit 1144 calculates the sum of the evaluationvalues for the respective indexes as an evaluation value E as shown inthe following Expression (26).

[Math. 28]

E=w _(a) E _(a) +w _(r) E _(r) + . . . +w _(c) E _(c)  (26)

In Expression (26), the evaluation value Ea is the evaluation value ofthe average amplitude Aav supplied from the amplitude evaluation unit1141, the evaluation value Er is the evaluation value of the averagepulsation interval Dav supplied from the number of pulsations evaluationunit 1142, and the evaluation value Ec is the evaluation value of theclassification process results supplied from the classification resultevaluation unit 1143. In addition, weight coefficients Wa, Wr, and Weare coefficients that weight the evaluation values Ea, Er, and Ec.

As described above, the evaluation integrating unit 1144 may arbitrarilyweight and integrate the evaluation values for the respective indexesand thus may quantitatively evaluate the object of evaluation with morevarious references.

The evaluation integrating unit 1144 outputs the evaluation value Ecalculated as above to the outside of the evaluation unit 1104 as anevaluation value 1150 of the object of evaluation.

The evaluation value 1150 output from the evaluation unit 1104 isdisplayed on a monitor as, for example, text information or imageinformation for a user and the like, or is output to another device (notshown) which performs an arbitrary process using the evaluation value1150. In addition, the evaluation value 1150 may also be recorded on arecording medium (not shown).

In this manner, the evaluation unit 1104 may quantitatively evaluatemore various indexes using more various methods. Accordingly, theevaluation unit 1104 may evaluate the object of evaluation(cardiomyocytes) more accurately.

[Flow of Evaluation Process]

Next, an example of the flow of an evaluation process performed by thecultured cardiomyocyte evaluation apparatus 1100 will be described withreference to the flowchart of FIG. 21.

When the evaluation process is started, the imaging unit 1101 of thecultured cardiomyocyte evaluation apparatus 1100 images an object ofevaluation in Step S1001. In Step S1002, the evaluation object imagedata generating and recording unit 1102 generates evaluation objectimage data from an image signal obtained through the imaging in StepS1001.

In Step S1003, the evaluation index data generating unit 1103 generatesevaluation index data which is data of various indexes for evaluatingthe motions of the object of evaluation from the evaluation object imagedata generated in Step S1002. In Step S1004, the evaluation unit 1104evaluates the motions of the object of evaluation using the evaluationindex data generated in Step S1003 and calculates an evaluation value.

In Step S1005, the evaluation unit 1104 outputs the evaluation valuecalculated in Step S1004 and ends the evaluation process.

[Flow of Evaluation Index Data Generation Process]

Next, an example of the flow of the evaluation index data generationprocess performed in Step S1003 of FIG. 21 will be described withreference to the flowchart of FIG. 22.

When the evaluation index data generation process is started, the motiondetecting unit 310 of the evaluation index data generating unit 1103detects the motions of the object of evaluation for each of blocks inStep S1021 and generates motion vectors. In Step S1022, the motiondetection data storage unit 320 stores the motion vector of each of theblocks generated in Step S1021.

In Step S1023, the motion detecting unit 310 determines whether or notmotion detection is performed for a predetermined evaluation durationdetermined in advance. In a case where it is determined that a frameimage which is not subjected to motion detection is present in thepredetermined evaluation duration, the motion detecting unit 310 returnsthe process to Step S1021 and repeats motion detection for a frame imageas a new object of the process.

In addition, in Step S1023, in a case where it is determined that motiondetection is performed on all the frame images as the object of theprocess in the predetermined evaluation duration, the motion detectingunit 310 moves the process to Step S1024.

In Step S1024, the feature amount calculating unit 1123 calculatesfeature amounts regarding the motions of the object of evaluation suchas the average amplitude Aav and the average pulsation interval Dav fromthe motion vectors stored in Step S1022. In Step S1025, the motionfeature amount data history storage memory 1125 stores the featureamounts calculated in Step S1024 as motion feature amount data.

In Step S1026, the classification processing unit 1124 classifies theblocks on the basis of the feature amounts calculated in Step S1024. InStep S1027, the motion feature amount data history storage memory 1125stores the classification results obtained in Step S1026 as motionfeature amount data.

In Step S1028, the feature amount calculating unit 1123 determineswhether or not calculation of the feature amounts is repeated apredetermined number of times (for example, S times) determined inadvance, and in a case where it is determined that the predeterminednumber of times has not been reached, returns the process to Step S1021and repeats the subsequent process. In addition, in Step S1028, in acase where it is determined that calculation of feature amounts isrepeated the predetermined number of times, the feature amountcalculating unit 1123 moves the process to Step S1029.

In Step S1029, the motion feature amount data history storage memory1125 outputs the motion feature amount data held therein to theevaluation unit 1104 as the evaluation index data. When the process ofStep S1029 is ended, the motion feature amount data history storagememory 1125 ends the evaluation index data generation process, returnsthe process to Step S1003 of FIG. 21, and performs the process afterStep S1004.

[Flow of Motion Evaluation Process]

Next, an example of the flow of the motion evaluation process performedin Step S1004 of FIG. 21 will be described with reference to theflowchart of FIG. 23.

When the motion evaluation process is started, the amplitude evaluationunit 1141 of the evaluation unit 1104 evaluates the amplitude of themotions of the object of evaluation on the basis of the evaluation indexdata regarding amplitudes in Step S1041, and calculates an evaluationvalue Ea thereof.

In Step S1042, the number of pulsations evaluation unit 1142 evaluatesthe number of pulsations per unit time of the motions of the object ofevaluation on the basis of the evaluation index data regarding thenumber of pulsations per unit time, and calculates an evaluation valueEr thereof.

In Step S1043, the classification result evaluation unit 1143 evaluatesthe result of the classification of each of the blocks performedaccording to the motions of the object of evaluation, on the basis ofthe evaluation index data regarding classification results, andcalculates an evaluation value Ec thereof.

In Step S1044, the evaluation integrating unit 1144 integrates theevaluation values of the respective indexes and calculates an evaluationvalue E of the object of evaluation.

When the evaluation value of the object of evaluation is calculated, theevaluation integrating unit 1144 ends the motion evaluation process,returns the process to Step S1004 of FIG. 21, and performs the processafter Step S1005.

In this manner, by performing various processes, the culturedcardiomyocyte evaluation apparatus 1100 may evaluate the object ofevaluation (for example, the motions of cells) more quantitatively. Inaddition, since the motion vector is used to generate the indexes, morevarious indexes may be obtained easily and in a noninvasive manner. Thatis, the object of evaluation (for example, the motions of cells) may bemore correctly evaluated.

4. Fourth Embodiment [Application to Another Evaluation]

In addition, by evaluating the cooperativity of the motions of theobject of evaluation, evaluation of other objects that have an impact onthe motions of the object of evaluation (for example, administration ofgas, liquids, or solids and the like), arbitrary environmentalconditions (for example, temperature, humidity, pressure, brightness,vibration, magnetic field, and the like), and the like may be performed.

Pulsations of various regions obtained by analyzing a phase differenceobservation moving image of cultured cardiomyocytes show cooperativepulsations depending on the number of days of culture and showvariations according to the administration of various drugs. Bydetecting such variations according to certain methods, it becomespossible to evaluate the toxicity, the effects, and the like of drugs inadvance when the drugs are discovered, which has been receivingattention in recent years.

According to the related art, for example, there is a method ofdetecting the external field potential of cells using an electrodedisposed on the bottom of a culture dish and ascertaining the pulsationbehavior of the cells according to the membrane potential change in thecells. In addition, there is also a method of inputting a fluorescentpigment that emits light by combining with calcium in cells anddetecting the calcium concentration that varies with cell excitability(action potential), thereby detecting the pulsation rhythm of the cellsand evaluating the information propagation pattern of the cells.

In such methods, specific culture dishes are needed, the fluorescentpigment is expensive, and inputting the fluorescent pigment iscomplicated and takes time. Therefore, there are many problems withsimple and noninvasive monitoring of cells.

Here, as described above, using the method of detecting and evaluatingthe motions of cells, evaluation of contraction and relaxationelongation in cell pulsations due to drug administration is performed,and the toxicity and the like of the drugs are evaluated using theevaluation results. The pulsations of cardiomyocytes includecontractions and relaxations. For example, when the passage of ions to acalcium channel of cells is impeded, the time of the relaxation iselongated (it becomes difficult to return from a contracted state).

By evaluating the elongation of the relaxation of the cells, an impacton the cardiomyocytes by the administered drug may be evaluated. Byperforming the evaluation of the motions of cells through image analysisas described above, changes in the pulsation behavior of the cells maybe ascertained without adding a reagent such as a certain fluorescentpigment to cells and without using special culture dishes, and thus thetoxicity and the like of drugs may be easily and accurately evaluated.

[Drug Evaluation Apparatus]

FIG. 24 is a block diagram illustrating a main configuration example ofa drug evaluation apparatus. The drug evaluation apparatus 1300illustrated in FIG. 24 is an apparatus that evaluates the impact(effects, side effects, and the like) of a drug using the motions of thecultured cardiomyocytes 500 to which the drug is administered.

As illustrated in FIG. 24, the drug evaluation apparatus 1300 has theimaging unit 1101 and the evaluation object image data generating andrecording unit 1102 as in the cultured cardiomyocyte evaluationapparatus 1100 of FIG. 13. The imaging unit 1101 images the culturedcardiomyocytes 500 before drug administration and after drugadministration.

The evaluation object image data generating and recording unit 1102generates evaluation object image data on the basis of an image signalsupplied from the imaging unit 1101, records and saves the generatedevaluation object image data in, for example, a recording mediumtherein. That is, the evaluation object image data regarding each ofmoving images of the cultured cardiomyocytes 500 before and after drugadministration is generated.

In addition, the drug evaluation apparatus 1300 has an evaluation indexdata generating unit 1303 instead of the evaluation index datagenerating unit 1103 of the cultured cardiomyocyte evaluation apparatus1100 and further has an evaluation unit 1304 instead of the evaluationunit 1104.

The evaluation index data generating unit 1303 acquires the evaluationobject image data from the evaluation object image data generating andrecording unit 1102. The evaluation index data generating unit 1303generates the evaluation index data using the acquired evaluation objectimage data and supplies this to the evaluation unit 1304.

More specifically, the evaluation index data generating unit 1303performs, for example, between frame images of the evaluation objectimage data which are the moving images of the cultured cardiomyocytes500, motion detection (generation of motion vectors) of the culturedcardiomyocytes 500 for each of the blocks which are partial regions intowhich the entire region of the frame image is divided. That is, theevaluation index data generating unit 1303 performs the motion detectionof each of the blocks for a predetermined duration (for a predeterminednumber of frames). This duration may be a time of the moving imagesimaged by the imaging unit 1101 or may be shorter than the time.

The evaluation index data generating unit 1303 further obtains a motionamount (the length of the motion vector) of each of the generated motionvectors. That is, the evaluation index data generating unit 1303generates the motion amounts of each of the frames of each of the blocksfor a predetermined duration.

When the motion amounts of the frames of a certain block obtained by theevaluation index data generating unit 1303 are arranged in time series,for example, the graph shown in FIG. 25 is obtained. FIG. 25 is adiagram showing a form of a change in the motion amount of a certainblock with respect to time obtained by the evaluation index datagenerating unit 1303, that is, an example of a form of the pulsations ofcells.

In the graph of FIG. 25, the horizontal axis represents the elapsed time(the number of frames), and the vertical axis represents the motionamount (pixels/frame). A curve 1311 (before) represents the pulsationsof the cultured cardiomyocytes 500 before drug administration, and acurve 1312 (after) represents the pulsations of the culturedcardiomyocytes 500 after drug administration. In addition, in the graphof FIG. 25, a waveform of a single pulsation (a contraction and arelaxation) is shown.

The pulsations of cardiomyocytes include contractions and relaxations,and in the curve 1311 and the curve 1312, the crest formed on the leftmeans a “contraction” operation, and the crest formed on the right meansa “relaxation” operation. A point P1-1 represents the peak of thecontraction of the curve 1311 (before), and a point P1-2 represents thepeak of the relaxation of the curve 1311 (before). A point P2-1represents the peak of the contraction of the curve 1313 (after), and apoint P2-2 represents the peak of the relaxation of the curve 1312(after).

In general, the relaxation of a cardiac muscle corresponds to a T wavereferred in an electrocardiogram, and corresponds to repolarization of acardiac muscle cell membrane. Elongation of the T wave is elongation ofa time between a Q wave and the T wave and is generally called QTelongation. In a case where this symptom appears, a possibility ofarrhythmia is pointed out. For example, when the passage of ions to acalcium channel is impeded by a drug administered to the culturedcardiomyocytes 500, such QT elongation occurs. For example, it is knownthat DL-sotalol (dl-sotalol) impedes the calcium channel. That is, whenDL-sotalol is administered to the cultured cardiomyocytes 500, due to achange in the calcium channel function that operates during therelaxation process, the relaxation process changes.

As shown in FIG. 25, the peak of the relaxation is shifted before andafter drug administration. More specifically, the time of the point P2-2is delayed (shifted) by a time d from the time of the point P1-2. Thatis, occurrence of QT elongation due to the drug administration (forexample, a change in the calcium channel function due to theadministration of DL-sotalol) may be confirmed.

In this manner, by comparing changes in the motion vectors (or themotion amounts thereof) of the pulsations (contractions and relaxations)of the cells with respect to time before and after drug administration,the effect, the toxicity, and the like of drugs may be evaluated.

In addition, observation of the QT elongation is achieved by potentialmeasurement according to the related art. However, a dedicated culturedish having an electrode is needed. In addition, in pulsation imagingusing calcium, basically, the peaks only on the left are observed, andit is difficult to observe the peaks on the right. Therefore, this isnot suitable for the evaluation of the elongation. Contrary to this, inthe case of the method of the present technique described above, achange in the cell pulsation behavior may be ascertained without addinga reagent such as a fluorescent pigment to cells and without usingspecial culture dishes. That is, easy, noninvasive, and low-costevaluation may be performed.

In order to perform the evaluation of the QT elongation, the evaluationindex data generating unit 1303 further calculates feature amounts forwaveform comparison such as a motion amount group arranged in timeseries, the coordinates of the point P2-1 and the point P2-2, and thetime d, from the generated motion amount (the length of the motionvector) of each of the motion vectors, and supplies the feature amountsto the evaluation unit 1304 as the evaluation index data.

The evaluation unit 1304 generates an image of the supplied evaluationindex data, performs quantitative evaluation thereon, or calculates andoutputs an evaluation value for the motions of the culturedcardiomyocytes 500.

More specifically, the evaluation unit 1304 displays a graph imageshowing a pulsation pattern as shown in, for example, FIG. 25 orperforms threshold determination of the time d that represents thedegree of QT elongation, thereby determining present or absence of theQT elongation.

In addition, as a matter of course, the graph shown in FIG. 25 is anexample of generation of an image, and besides, the cell pulsationpattern may be expressed by arbitrary images such as a bar chart, adistribution chart, and a schematic diagraph. In addition, the evaluateddrug is arbitrary.

Hereinafter, details of each unit will be described.

[Evaluation Index Data Generating Unit]

FIG. 26 is a block diagram illustrating a main configuration example ofthe evaluation index data generating unit 1303. As illustrated in FIG.26, the evaluation index data generating unit 1303 has the motiondetecting unit 310 and generates a motion vector for each of the blocksby performing motion detection between frame images of the evaluationobject image data 600 (moving images).

In addition, the evaluation index data generating unit 1303 has a motionamount absolute value calculating unit 1321, a motion amount absolutevalue storage unit 1322, a feature amount calculating unit 1323, and afeature amount storage unit 1324.

The motion amount absolute value calculating unit 1321 calculates themotion amount (the absolute value of the length of a motion vector)(hereinafter, also called a motion amount absolute value) for each ofthe motion vectors detected by the motion detecting unit 310. The motionamount absolute value calculating unit 1321 stores the calculated motionamount absolute values in the motion amount absolute value storage unit1322.

The motion amount absolute value storage unit 1322 stores the motionamount absolute value of each of blocks between the entire frames of theevaluation object image data 600. For example, in a case where aplurality of evaluation object image data are preset like before andafter drug administration, the motion amount absolute value storage unit1322 stores the motion amount absolute value for each of the evaluationobject image data.

The feature amount calculating unit 1323 calculates predeterminedfeature amounts used for evaluation using the motion amount absolutevalues stored in the motion amount absolute value storage unit 1322. Thefeature amount storage unit 1324 stores the feature amounts calculatedby the feature amount calculating unit 1323. The feature amounts aresupplied to the evaluation unit 1304 as evaluation index data 800 at apredetermined timing or in response to the request of the evaluationunit 1304 and the like.

[Evaluation Unit]

FIG. 27 is a block diagram illustrating a main configuration example ofthe evaluation unit 1304. As illustrated in FIG. 27, the evaluation unit1304 has a feature amount acquisition unit 1341, a feature comparisonunit 1342, a display unit 1343, and an output unit 1344.

The feature amount acquisition unit 1341 acquires predetermined featureamounts (for example, feature amounts of an object of evaluation(cultured cardiomyocytes 500) designated by a user) from the evaluationindex data generating unit 1303 (feature amount storage unit 1324) asthe evaluation index data 800. The feature amount acquisition unit 1341supplies the acquired feature amounts to the display unit 1343 so as tobe displayed or supplies the feature amounts to the output unit 1344 soas to be supplied to another device. In addition, the feature amountacquisition unit 1341 supplies the acquired feature amounts to thefeature comparison unit 1342.

The feature comparison unit 1342 quantitatively evaluates the suppliedfeature amounts. For example, the feature comparison unit 1342 comparesthe feature amounts of a plurality of cultured cardiomyocytes 500 toeach other, for example, before and after drug administration orcompares the feature amounts to a predetermined threshold, therebyperforming quantitative evaluation. The feature comparison unit 1342supplies the comparison results to the display unit 1343 to be displayedor supplies the comparison results to the output unit 1344 to besupplied to another device.

The display unit 1343 has a display device such as a monitor andgenerates an image of the data supplied from the feature amountacquisition unit 1341 or the feature comparison unit 1342 so as todisplay the image on the display device. For example, using the motionamounts acquired by the feature amount acquisition unit 1341, thedisplay unit 1343 generates and displays a graph as shown in, forexample, FIG. 25. In addition, for example, the display unit 1343generates an image of the evaluation results supplied from the featurecomparison unit 1342 so as to be displayed.

The output unit 1344 has an interface such as an external terminal andoutputs the data supplied from the feature amount acquisition unit 1341or the feature comparison unit 1342 to an external device, a network,and the like.

As described above, as the evaluation unit 1304 evaluates the pulsationpattern, the drug evaluation apparatus 1300 may evaluate the impact ofdrug administration on the pulsations of cardiomyocytes easily and in anoninvasive manner.

In addition, it is described above that the evaluation unit evaluatesthe occurrence of QT elongation. However, parameters to be evaluated maybe something other than that. That is, the calculated feature amountsare arbitrary. For example, in the graph of FIG. 25, a difference in themotion amount between the point P1-2 and the point P2-2 may be a featureamount. In addition, a difference in the time or motion amount betweenthe point P1-1 and the point P2-1 may be a feature amount. Furthermore,for example, the width of the crest of contraction or the width of thecrest of relaxation may be a feature amount. As a matter of course,parameters other than those may also be feature amounts.

In addition, the evaluation unit 1304 may perform such evaluation on theentire blocks in an objection region or on a part of the blocks.Moreover, the evaluation unit 1304 may perform such evaluation on allpulsations in an observation duration or on a part of the pulsations.

[Flow of Evaluation Process]

Next, an example of the flow of the evaluation process performed by thedrug evaluation apparatus 1300 will be described with reference to theflowchart of FIG. 28.

When the evaluation process is started, the imaging unit 1101 of thedrug evaluation apparatus 1300 images the object of evaluation in StepS1301. In Step S1302, the evaluation object image data generating andrecording unit 1102 generates the evaluation object image data from animage signal obtained through the imaging in Step S1301.

In Step S1303, the evaluation index data generating unit 1303 generatesthe evaluation index data using the evaluation object image datagenerated in Step S1302. In Step S1304, the evaluation unit 1304observes the pulsation pattern (for example, QT elongation) of thecultured cardiomyocytes 500 before and after drug administration usingthe evaluation index data generated in Step S1303, thereby evaluatingthe impact of the drug.

In Step S1305, the output unit 1344 of the evaluation unit 1304 outputsthe evaluation value calculated in Step S1304 to the outside of the drugevaluation apparatus 1300 and ends the evaluation process. In addition,in Step S1305, instead of the outputting of the output unit 1344, asdescribed above, the display unit 1343 may generate an image of theevaluation value and display the image on the display device. Inaddition, as described above, the display unit 1343 may generate animage of various feature amounts calculated by the process in Step S1303so as to be displayed on the display device, or the output unit 1344 mayoutput the various feature amounts to the outside of the drug evaluationapparatus 1300.

[Flow of Evaluation Index Data Generation Process]

Next, an example of the flow of the evaluation index data generationprocess performed in Step S1303 of FIG. 28 will be described withreference to the flowchart of FIG. 29.

When the evaluation index data generation process is started, the motiondetecting unit 310 of the evaluation index data generating unit 1303detects the motion of the object of evaluation for each of the blocks inStep S1321 and generates the motion vector. In Step S1322, the motionamount absolute value calculating unit 1321 calculates the motion amountabsolute value of the motion vector generated in Step S1321.

In Step S1323, the motion amount absolute value storage unit 1322 storesthe motion amount absolute value calculated in Step S1322.

In Step S1324, the motion detecting unit 310 determines whether or notmotion detection is performed for a predetermined duration (evaluationsection) determined in advance. In a case where it is determined that aframe image that is not subjected to motion detection is present in thepredetermined evaluation section, the motion detecting unit 310 returnsthe process to Step S1321 and repeats motion detection for a frame imageas new object of the process.

In addition, in Step S1324, in a case where it is determined that motiondetection is performed on all the frame images as the object of theprocess in the predetermined evaluation section, the motion detectingunit 310 moves the process to Step S1325.

In Step S1325, the feature amount calculating unit 1323 calculatesfeature amounts using the motion amount absolute values stored in StepS1323. In Step S1326, the feature amount storage unit 1324 stores thefeature amounts calculated in Step S1325.

In Step S1327, the feature amount calculating unit 1323 determineswhether or not calculation of the feature amounts is repeated apredetermined number of times (for example, S times) determined inadvance, and in a case where it is determined that the predeterminednumber of times has not been reached, returns the process to Step S1321and repeats the subsequent process. In addition, in Step S1327, in acase where it is determined that calculation of feature amounts isrepeated the predetermined number of times, the feature amountcalculating unit 1323 ends the evaluation index data generation process,returns the process to FIG. 28, and performs the process after StepS1304.

[Flow of Impact Evaluation Process]

Next, an example of the flow of the impact evaluation process performedin Step S1304 of FIG. 28 will be described with reference to theflowchart of FIG. 30.

When the impact evaluation process is started, the feature amountacquisition unit 1341 of the evaluation unit 1304 acquires desiredmotion vectors from the feature amount storage unit 1324 in Step S1341.

In Step S1342, the feature comparison unit 1342 compares the acquiredfeature amounts between objects in Step S1341. When the process of StepS1342 is ended, the feature comparison unit 1342 ends the impactevaluation process, returns the process to FIG. 28, and performs theprocess of Step S1305.

As described above, by obtaining the feature amounts regarding changesin the motion amounts of the objects of observation subjected to themotion detection with respect to time by the evaluation unit 1304, thedrug evaluation apparatus 1300 may easily evaluate the impact of thedrug administration on the pulsations of cardiomyocytes. Since thismethod does not use special culture dishes or fluorescent reagents,simple, noninvasive, and low-cost evaluation is possible, and the methodis appropriate for automation. In addition, in the case of this method,an observation region may be a relatively narrow range of about, forexample, 0.6 square millimeters, and tests are possible with a smallnumber of cells and a small amount of reagent. In addition, evaluationmay be sufficiently achieved with a high-density culture plate(1536-hole plate (1.7 mm-diameter/1-well) or a 384-hole plate (3.6mm-diameter/1-well) which is generally commercialized, and the method isappropriate for initial screening when drugs are discovered. Moreover,the present technique may also be applied to a case of evaluatinganything that is able to be evaluated by observing the culturedcardiomyocytes 500.

[Example of Pulsation Change Due to Drug Administration]

FIG. 31 is a diagram showing an example of forms of pulsations beforeand after drug administration. All the 8 graphs shown in FIG. 31 areobservation results of forms (changes in the motion amount absolutevalue with respect to time) of pulsations of a predetermined part in anobservation region of the cultured cardiomyocytes 500. The horizontalaxis represents the time (sec), and the vertical axis represents themotion amount absolute value (pixcel/frame) between frames. That is, theamplitudes shown in each of the graphs represents the pulsations of thecultured cardiomyocytes 500.

The graphs on the left of FIG. 31 represent forms of the pulsationsbefore drug administration, and the graphs on the right represent formsof the pulsations after drug administration (after a predetermined timeelapses after the administration).

In the case of the example of FIG. 31, the uppermost graphs show theform of the pulsations before and after the administration of an organicsolvent (control) (for example, dimethyl sulfoxide). In addition, thesecond graphs from the top show the form of the pulsations before andafter the administration of aspirin (acetylsalicylic acid). Moreover,the third graphs from the top show the form of the pulsations before andafter the administration of DL-sotalol (dl-sotalol). In addition, thelowermost 3D plots show the form of the pulsations before and after theadministration of 18-β-glycyrrhetinic acid (18-β-Glycyrrhetinic acid).

As shown in the graphs of FIG. 31, the organic solvent or the aspirindoes not have a significant impact on the interval of the pulsations ofthe cardiomyocytes. That is, as shown in the graphs on the left of FIG.31, before the drug administration, pulsations are repeated at asubstantially constant interval (the peak interval is substantiallyconstant), and even when the organic solvent or the aspirin isadministered to the cardiomyocytes, as shown in the first and secondgraphs from the top on the right of FIG. 31, pulsations are repeated atsubstantially the same interval as that of the drug administration andat a constant interval. That is, in this case, the rhythm of thepulsations is not substantially changed (the peak interval is notchanged).

Contrary to this, when DL-sotalol is administered, the function of thecalcium channel is degraded, and not only the waveform of the relaxation(the width of the pulsation) changes (becomes unstable), but also thetiming of the pulsation becomes unstable (the peak interval hasvariations) as shown in the third graph from the top on the right ofFIG. 31. In addition, the motion amount absolute value at the peaks alsobecomes unstable (has variations).

In addition, it is known that 18-β-glycyrrhetinic acid impedes a gapjunction. Even in a case where the 18-β-glycyrrhetinic acid isadministered, as shown in the fourth graph from the top on the right ofFIG. 31, the timing of the pulsation and the motion amount absolutevalue at the peaks become unstable (have variations).

In addition, FIG. 32 is a diagram showing an example of forms ofvariations of the pulsations before and after drug administration. Inall the 8 graphs shown in FIG. 32, waveforms (a plurality of pulsations)of pulsations repeated in a predetermined part in an observation regionof the cultured cardiomyocytes 500 overlap.

As in the case of FIG. 31, the horizontal axis in each of the graphsrepresents the time (sec), and the vertical axis represents the motionamount absolute value (pixcel/frame) between frames. In addition, thegraphs on the left represent the pulsations before the drugadministration, and the graphs on the right represent the pulsationsafter the drug administration (after a predetermined time elapses afterthe administration).

In addition, as in the case of FIG. 31, the administered drugs are, inorder from the top, an organic solvent (control), aspirin(acetylsalicylic acid), DL-sotalol, and 18-β-glycyrrhetinic acid(18-β-Glycyrrhetinic acid).

As shown in the graphs of FIG. 32, even though the organic solvent orthe aspirin is administered, the pulsations of the cardiomyocytes do nothave significant variations like before the administration.

Contrary to this, when DL-sotalol is administered, the function of thecalcium channel is degraded, and as shown in the third graph from thetop on the right of FIG. 31, the waveform (the magnitude of the peak,the time of appearance of the peak, the number of times of appearance ofthe peak, the width (QT elongation), and the like) of the relaxationmainly has significant variations. In addition, the height of the peaksin the waveform of the contraction also has significant variations.

In addition, 18-β-glycyrrhetinic acid impedes the function of a gapjunction, and as shown in the fourth graph from the top on the right ofFIG. 31, the height of the peak in the waveform of the contraction hassignificant variations.

Using the drug evaluation apparatus 1300, the impact of the drugadministration on the pulsations may be perceived easily and in anoninvasive manner.

As described above, by observing the changes in the forms of thepulsations due to the drug administration for specific cells (a specificpartial region) in the observation region of the cultured cardiomyocytes500, information that is obtained only by observing the correlation inthe pulsations between the cells may be obtained. Therefore, drugevaluation may be performed with different indexes from those of a caseof observing the correlation in the pulsations between cells.

5. Fifth Embodiment [Personal Computer]

A series of the processes described above may be performed in hardwareor performed in software. In this case, for example, a personal computeras illustrated in FIG. 33 may be configured.

In FIG. 33, a CPU (Central Processing Unit) 1501 of the personalcomputer 1500 performs various processes according to programs stored ina ROM (Read Only Memory) 1502 or programs loaded on a RAM (Random AccessMemory) 1503 from a storage unit 1513. The RAM 1503 appropriately storesdata and the like needed for the CPU 1501 to perform the variousprocesses.

The CPU 1501, the ROM 1502, and the RAM 1503 are connected to each othervia a bus 1504. An input and output interface 1510 is also connected tothe bus 1504.

To the input and output interface 1510, an input unit 1511 made of akeyboard, a mouse, or the like, an output unit 1512 made of a displaysuch as a CRT (Cathode Ray Tube) or an LCD (Liquid Crystal Display), aspeaker, or the like, the storage unit 1513 configured of a hard disk orthe like, and a communication unit 1514 configured of a modem or thelike are connected. The communication unit 1514 performs a communicationprocess over a network including the Internet.

A drive 1515 is also connected to the input and output interface 1510 asnecessary, a removable medium 1521 such as a magnetic disk, an opticaldisc, a magneto-optical disc, or a semiconductor memory is appropriatelymounted, and computer programs read therefrom are installed on thestorage unit 1513 as necessary.

In the case where the series of processes described above is performedin software, programs that constitute the software are installed fromthe network or a recording medium.

The recording medium is, for example, as illustrated in FIG. 33,separately from the apparatus main body, configured not only as theremovable medium 1521 which is distributed to deliver programs to a userand is made of a magnetic disk (including a flexible disk) havingprograms recorded, an optical disc (including a CD-ROM (CompactDisc-Read Only Memory) and a DVD (Digital Versatile Disc)), amagneto-optical disc (including an MD (Mini Disc)), a semiconductormemory, or the like, but also the ROM 1502 which records programs thatare delivered to a user in a state of being assembled to the apparatusmain body, the hard disk included in the storage unit 1513, and thelike.

In addition, the programs performed by the computer may be programs thatare processed in time series according to the order described in thespecification, or may be programs that are processed in parallel or at anecessary timing such as when a call is made.

In addition, in the specification, the steps that describe the programsrecorded on a recording medium include not only processes performed intime series according to the described order, but also processes thatare not necessarily performed in time series but are performed inparallel or individually.

In addition, in the specification, the system represents the entireapparatus constituted by a plurality of devices (devices).

In addition, in the above description, the configuration described as asingle device (or a processing unit) may be divided to be configured asa plurality of devices (or processing units). Contrary to this, theconfiguration described as a plurality of devices (or processing units)in the above description may be summed to be configured as a singledevice (or processing unit). In addition, configurations other thanthose described above may also be added to the configuration of each ofthe devices (or each of the processing units). Moreover, when theconfiguration or the operation of the entire system is substantially thesame, a part of the configuration of a certain device (or processingunit) may be included in the configuration of another device (or anotherprocessing unit). That is, the present technique is not limited to theabove-described embodiments, and various modifications can be made in arange without departing from the gist of the present technique.

In addition, the present technique may employ the followingconfigurations.

(1) A data processing apparatus including: a motion detecting unit whichdivides a plurality of frame image data that form moving image datahaving an image content of an object that performs a periodic motioninto blocks according to an arrangement of a predetermined pixel countand detects time-series data of motions for each of the correspondingblocks;

a feature amount calculating unit which calculates at least one kind offeature amount for each of the blocks on the basis of the time-seriesdata of the motions for each of the detected blocks;

and a classification processing unit which generates classification datathat represents a result of classifying each of the blocks that form anyone of the plurality of frame image data into any one of a predeterminednumber of classification categories on the basis of the calculatedfeature amount.

(2) The data processing apparatus described in (1),

wherein the feature amount calculating unit calculates a plurality ofkinds of the feature amounts for each of the blocks, and theclassification unit generates the classification data on the basis ofthe plurality of kinds of the feature amounts calculated.

(3) The data processing apparatus described in (1) or (2),

wherein the feature amount calculating unit calculates an average motiondirection which is an average value of motion directions per unit timewithin a predetermined time as one kind of the feature amounts.

(4) The data processing apparatus described in any one of (1) to (3),

wherein the feature amount calculating unit calculates an average motionamount which is an average value of motion amounts per unit time withina predetermined time as one kind of the feature amounts.

(5) The data processing apparatus described in any one of (1) to (4),

wherein the feature amount calculating unit calculates an averageamplitude which is an average value of amplitudes with a predeterminedor higher motion amount obtained within a predetermined time as one kindof the feature amounts.

(6) The data processing apparatus described in any one of (1) to (5),

wherein the feature amount calculating unit calculates an averageacceleration which is an average value of accelerations of motions perunit time within a predetermined time as one kind of the featureamounts.

(7) The data processing apparatus described in any one of (1) to (6),

wherein the feature amount calculating unit calculates an average motioninterval which is an average value of time intervals at which anamplitude with a predetermined or higher motion amount is obtainedwithin a predetermined time as one kind of the feature amounts.

(8) The data processing apparatus described in any one of (1) to (7),

wherein the feature amount calculating unit calculates a motion startingtime which is a time from a predetermined timing to a timing at which anamplitude with a predetermined or higher motion amount is obtained asone kind of the feature amounts.

(9) The data processing apparatus described in any one of (1) to (8),

wherein the classification unit performs a process of calculating adistance between each of the plurality of templates having a combinationof other feature amounts and the block in correspondence to a pluralityof the classification categories, and classifying the block into any oneof the plurality of the classification categories on the basis of thecalculated distance, on each of the blocks.

(10) The data processing apparatus described in any one of (1) to (9),

wherein the classification unit classifies each of the blocks into anyone of a predetermined number of classification categories by performingclustering according to a k-means method on the basis of the featureamounts calculated corresponding to each of the blocks.

(11) A data processing method including:

a motion detecting step of dividing plurality of frame image data thatform moving image data having an image content of an object thatperforms a periodic motion into blocks according to an arrangement of apredetermined pixel count and detecting time-series data of motions foreach of the corresponding blocks;

a feature amount calculating step of calculating at least one kind offeature amount for each of the blocks on the basis of the time-seriesdata of the motions for each of the detected blocks; and

a classification processing step of generating classification data thatrepresents a result of classifying each of the blocks that form any oneof the plurality of frame image data into any one of a predeterminednumber of classification categories on the basis of the calculatedfeature amount.

(12) An image processing apparatus including:

a motion detecting unit which detects a motion of an object ofevaluation using an image of the object of evaluation;

an index data generating unit which, by using a motion vector thatrepresents the motion of the object of evaluation detected by the motiondetecting unit, generates index data that represents a feature of themotion of the object of evaluation and is used as an index forevaluating the object of evaluation; and

an evaluation value calculating unit which evaluates the index datagenerated by the index data generating unit and calculates an evaluationvalue.

(13) The image processing apparatus described in (12),

wherein the index data generating unit generates index data regarding amagnitude of an amplitude of the motion of the object of evaluation, andindex data regarding a frequency per unit time of a peak of the motionof the object of evaluation, and the evaluation value calculating unitcalculates an evaluation value that evaluates the magnitude of theamplitude of the motion of the object of evaluation using the index dataregarding the magnitude of the amplitude of the motion of the object ofevaluation generated by the index data generating unit, and furthercalculates an evaluation value that evaluates the frequency per unittime of the peak of the motion of the object of evaluation using theindex data regarding the frequency per unit time of the peak of themotion of the object of evaluation generated by the index datagenerating unit.

(14) The image processing apparatus described in (13),

wherein the index data regarding the magnitude of the amplitude of themotion of the object of evaluation is an average value of products of anormalized amplitude and a normalized dispersion of the amplitudes overan entire image of the object of evaluation.

(15) The image processing apparatus described in (13) or (14),

wherein the index data regarding the magnitude of the amplitude of themotion of the object of evaluation is a proportion of a region in whicha value of a product of a normalized amplitude and a normalizeddispersion of the amplitudes is higher than or equal to a predeterminedthreshold with respect to an entire image of the object of evaluation.

(16) The image processing apparatus described in any one of (13) to(15),

wherein the index data regarding the frequency per unit time of the peakof the motion of the object of evaluation is an average value ofproducts of a normalized number of the peaks per unit time and anormalized dispersion of the numbers of the peaks per unit time over anentire screen.

(17) The image processing apparatus described in any one of (13) to(16),

wherein the index data regarding the frequency per unit time of the peakof the motion of the object of evaluation is a proportion of a region inwhich a value of a product of a normalized number of the peaks per unittime and a normalized dispersion of the numbers of the peaks per unittime is higher than or equal to a predetermined threshold with respectto an entire image of the object of evaluation.

(18) The image processing apparatus described in any one of (13) to(17),

wherein the index data generating unit further generates index dataregarding a classification result of classifying each of partial regionsof the image of the object of evaluation on the basis of the featureamount of the motion of the object of evaluation, and

the evaluation value calculating unit further calculates an evaluationvalue that evaluates the classification result of the feature amount ofthe motion of the object of evaluation using the index data regardingthe classification result generated by the index data generating unit.

(19) The image processing apparatus described in any one of (12) to(18),

wherein the index data generating unit calculates a motion amount of theobject of evaluation detected by the motion detecting unit, and

the evaluation value calculating unit generates an image of a change inthe motion amount calculated by the index data generating unit withrespect to time so as to be displayed.

(20) The image processing apparatus described in (19), wherein the indexdata generating unit generates index data that represents a change dueto administration of a drug to a cardiomyocyte in a peak of a waveformrepresenting a relaxation of the cardiomyocyte which is the object ofevaluation, in the change in the calculated motion amount with respectto time, and

the evaluation value calculating unit evaluates the index datacalculated by the index data generating unit and calculates anevaluation value.

(21) The image processing apparatus described in any one of (12) to(20),

which further includes an imaging unit which images the object ofevaluation and obtains an image of the object of evaluation,

wherein the motion detecting unit detects the motion of the object ofevaluation using the image of the object of evaluation obtained by theimaging unit.

(22) The image processing apparatus described in any one of (12) to(21),

wherein the motion detecting unit detects the motion of the object ofevaluation between frame images in an evaluation duration with apredetermined length in the image of the object of evaluation which is amoving image.

(23) The image processing apparatus described in (22),

wherein the motion detecting unit repeats detection of the motion of theobject of evaluation in the evaluation duration a predetermined numberof times.

(24) The image processing apparatus described in any one of (12) to(23),

wherein the evaluation value calculating unit evaluates each of aplurality of kinds of the index data generated by the index datagenerating unit to calculate the evaluation value, and integrates thecalculated evaluation values, thereby calculating an evaluation valuethat evaluates the object of evaluation.

(25) The image processing apparatus described in any one of (12) to(24),

wherein the object of evaluation is a cell that moves spontaneously.

(26) The image processing apparatus described in any one of (12) to(25),

wherein the object of evaluation is a cultured cell generated byculturing a cell collected from a living body.

(27) An image processing method including:

detecting a motion of an object of evaluation using an image of theobject of evaluation by a motion detecting unit of an image processingapparatus;

by using a motion vector that represents the detected motion of theobject of evaluation, generating index data that represents a feature ofthe motion of the object of evaluation and is used as an index forevaluating the object of evaluation by an index data generating unit ofthe image processing apparatus; and

evaluating the generated index data and calculating an evaluation valueby an evaluation value calculating unit of the image processingapparatus.

(28) A program which causes a computer to function as:

a motion detecting unit which detects a motion of an object ofevaluation using an image of the object of evaluation; an index datagenerating unit which, by using a motion vector that represents thedetected motion of the object of evaluation, generates index data thatrepresents a feature of the motion of the object of evaluation and isused as an index for evaluating the object of evaluation; and

an evaluation value calculating unit which evaluates the generated indexdata and calculates an evaluation value.

REFERENCE SIGNS LIST

100 cultured cardiomyocyte evaluation system

-   -   110 imaging device    -   200 evaluation object image data generating and recording device    -   300 evaluation index data generating device    -   310 motion detecting unit    -   311 frame memory    -   312 motion vector calculating unit    -   320 motion detection data storage unit    -   330 feature amount calculating unit    -   340 classification processing unit    -   400 evaluation device    -   500 cultured cardiomyocyte    -   600 evaluation object image data    -   610 frame image data    -   611 block    -   700 motion detection data    -   710 frame unit motion detection data    -   800 evaluation index data    -   801 individual classification result data    -   1100 cultured cardiomyocyte evaluation apparatus    -   1101 imaging unit    -   1102 evaluation object image data generating and recording unit    -   1103 evaluation index data generating unit    -   1104 evaluation unit    -   1123 feature amount calculating unit    -   1124 classification processing unit    -   1125 motion feature amount data history storage memory    -   1141 amplitude evaluation unit    -   1142 number of pulsations evaluation unit    -   1143 classification result evaluation unit    -   1144 evaluation integrating unit    -   1300 drug evaluation apparatus    -   1303 evaluation index data generating unit    -   1304 evaluation unit    -   1341 feature amount acquisition unit    -   1342 feature comparison unit    -   1343 display unit    -   1344 output unit

1. An image processing apparatus comprising: a motion detecting unitwhich detects a motion of an object of evaluation using an image of theobject of evaluation; an index data generating unit which, by using amotion vector that represents the motion of the object of evaluationdetected by the motion detecting unit, generates index data thatrepresents a feature of the motion of the object of evaluation and isused as an index for evaluating the object of evaluation; and anevaluation value calculating unit which evaluates the index datagenerated by the index data generating unit and calculates an evaluationvalue.
 2. The image processing apparatus according to claim 1, whereinthe index data generating unit generates index data regarding amagnitude of an amplitude of the motion of the object of evaluation, andindex data regarding a frequency per unit time of a peak of the motionof the object of evaluation, and the evaluation value calculating unitcalculates an evaluation value that evaluates the magnitude of theamplitude of the motion of the object of evaluation using the index dataregarding the magnitude of the amplitude of the motion of the object ofevaluation generated by the index data generating unit, and furthercalculates an evaluation value that evaluates the frequency per unittime of the peak of the motion of the object of evaluation using theindex data regarding the frequency per unit time of the peak of themotion of the object of evaluation generated by the index datagenerating unit.
 3. The image processing apparatus according to claim 2,wherein the index data regarding the magnitude of the amplitude of themotion of the object of evaluation is an average value of products of anormalized amplitude and a normalized dispersion of the amplitudes overan entire image of the object of evaluation.
 4. The image processingapparatus according to claim 2, wherein the index data regarding themagnitude of the amplitude of the motion of the object of evaluation isa proportion of a region in which a value of a product of a normalizedamplitude and a normalized dispersion of the amplitudes is higher thanor equal to a predetermined threshold with respect to an entire image ofthe object of evaluation.
 5. The image processing apparatus according toclaim 2, wherein the index data regarding the frequency per unit time ofthe peak of the motion of the object of evaluation is an average valueof products of a normalized number of the peaks per unit time and anormalized dispersion of the numbers of the peaks per unit time over anentire screen.
 6. The image processing apparatus according to claim 2,wherein the index data regarding the frequency per unit time of the peakof the motion of the object of evaluation is a proportion of a region inwhich a value of a product of a normalized number of the peaks per unittime and a normalized dispersion of the numbers of the peaks per unittime is higher than or equal to a predetermined threshold with respectto an entire image of the object of evaluation.
 7. The image processingapparatus according to claim 2, wherein the index data generating unitfurther generates index data regarding a classification result ofclassifying each of partial regions of the image of the object ofevaluation on the basis of the feature amount of the motion of theobject of evaluation, and the evaluation value calculating unit furthercalculates an evaluation value that evaluates the classification resultof the feature amount of the motion of the object of evaluation usingthe index data regarding the classification result generated by theindex data generating unit.
 8. The image processing apparatus accordingto claim 1, wherein the index data generating unit calculates a motionamount of the object of evaluation detected by the motion detectingunit, and the evaluation value calculating unit generates an image of achange in the motion amount calculated by the index data generating unitwith respect to time so as to be displayed.
 9. The image processingapparatus according to claim 8, wherein the index data generating unitgenerates index data that represents a change due to administration of adrug to a cardiomyocyte in a peak of a waveform representing arelaxation of the cardiomyocyte which is the object of evaluation, inthe change in the calculated motion amount with respect to time, and theevaluation value calculating unit evaluates the index data calculated bythe index data generating unit and calculates an evaluation value. 10.The image processing apparatus according to claim 1, further comprisingan imaging unit which images the object of evaluation and obtains animage of the object of evaluation, wherein the motion detecting unitdetects the motion of the object of evaluation using the image of theobject of evaluation obtained by the imaging unit.
 11. The imageprocessing apparatus according to claim 1, wherein the motion detectingunit detects the motion of the object of evaluation between frame imagesin an evaluation duration with a predetermined length in the image ofthe object of evaluation which is a moving image.
 12. The imageprocessing apparatus according to claim 11, wherein the motion detectingunit repeats detection of the motion of the object of evaluation in theevaluation duration a predetermined number of times.
 13. The imageprocessing apparatus according to claim 1, wherein the evaluation valuecalculating unit evaluates each of a plurality of kinds of the indexdata generated by the index data generating unit to calculate theevaluation value, and integrates the calculated evaluation values,thereby calculating an evaluation value that evaluates the object ofevaluation.
 14. The image processing apparatus according to claim 1,wherein the object of evaluation is a cell that moves spontaneously. 15.The image processing apparatus according to claim 1, wherein the objectof evaluation is a cultured cell generated by culturing a cell collectedfrom a living body.
 16. An image processing method comprising: detectinga motion of an object of evaluation using an image of the object ofevaluation by a motion detecting unit of an image processing apparatus;by using a motion vector that represents the detected motion of theobject of evaluation, generating index data that represents a feature ofthe motion of the object of evaluation and is used as an index forevaluating the object of evaluation by an index data generating unit ofthe image processing apparatus; and evaluating the generated index dataand calculating an evaluation value by an evaluation value calculatingunit of the image processing apparatus.
 17. A program which causes acomputer to function as: a motion detecting unit which detects a motionof an object of evaluation using an image of the object of evaluation;an index data generating unit which, by using a motion vector thatrepresents the detected motion of the object of evaluation, generatesindex data that represents a feature of the motion of the object ofevaluation and is used as an index for evaluating the object ofevaluation; and an evaluation value calculating unit which evaluates thegenerated index data and calculates an evaluation value.